Review ArticleImage Inpainting Methods Evaluation and Improvement

Raluca Vreja and Remus Brad

Computer Science Department Lucian Blaga University of Sibiu B-dul Victoriei 10 550024 Sibiu Romania

Correspondence should be addressed to Remus Brad remusbradulbsibiuro

Received 21 January 2014 Revised 5 July 2014 Accepted 7 July 2014 Published 17 July 2014

Academic Editor Stefano Berretti

Copyright copy 2014 R Vreja and R BradThis is an open access article distributed under the Creative CommonsAttribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

With the upgrowing of digital processing of images and film archiving the need for assisted or unsupervised restoration requiredthe development of a series of methods and techniques Among them image inpainting is maybe the most impressive and usefulBased on partial derivative equations or texture synthesis many other hybrid techniques have been proposed recently The needfor an analytical comparison beside the visual one urged us to perform the studies shown in the present paper Starting with anoverview of the domain an evaluation of the five methods was performed using a common benchmark and measuring the PSNRConclusions regarding the performance of the investigated algorithms have been presented categorizing them in function of therestored image structure Based on these experiments we have proposed an adaptation of Oliveirarsquos and Hadhoudrsquos algorithmswhich are performing well on images with natural defects

1 Introduction

The process of region filling following the loss of informationin digital images represents an important aspect in imageprocessing Image inpainting refers to restoration methodsused to remove damage or unwanted objects from an imagein a natural manner such that a neutral observer wouldnot notice any changes and consider the result as being theoriginal image

Restoration methods can be classified in three majorcategories structural inpainting techniques textural inpaint-ing methods and hybrid methods In spite of these threecategories methods may be divided in partial derivativeequations (PDE) based algorithms semiautomatic inpaint-ing methods texture synthesis methods algorithms basedon modelstemplates and hybrid techniques depending onspecific characteristics [1ndash3]

Based on the PDE model the first approach belongsto Bertalmio et al [4] who proposed a method in whichthe information is propagated in the occluded area throughisophote lines that cross the edges The algorithm is efficientwhen applied to images with narrow damages since it makesuse of anisotropic diffusion which leads to blurring effectsThe major disadvantage of this method is represented bythe fact that it cannot reconstruct textures [5] In the same

category fall the methods developed by Taschler [2] Theauthors have proposed an algorithm based on partial differ-ential equations of second order which uses diffusion andan improved version of the previous one [6] The significantproblem was the same namely the algorithm was not able toreconstruct textures Tschumperle and Deriche [7] presenteda method which makes use of high-order partial differentialequations Although this was not intended to be an imagerestoration technique it leads to good results for images withnarrow damages and occluded regions of small area

Regarding the category of semiautomatic inpaintingmethods Sun et al [8] proposed a technique that requirestwo steps to perform the restoration In the first step theuser has to sketch the object contours in the occluded areastarting from the outside to the inside and then apply atexture synthesis process that uses images or blocks of pixelsas a source for the texture The algorithm proposed byOliviera et al [9] uses an isotropic diffusion process aimedto preserve the contours The edges are excluded from themask in order not be affected by smoothing Due to theiterative process some blurring effects may be obtainedTelea [10] tries to provide an improvement by estimatingthe pixel value based on the restored pixel neighborhoodwith the clear advantage of applying the inpainting processonly once for each pixel comparative to iterative methods

Hindawi Publishing Corporatione Scientific World JournalVolume 2014 Article ID 937845 11 pageshttpdxdoiorg1011552014937845

2 The Scientific World Journal

A seam carving method was presented in [11] overcomingthe time consuming disadvantage of this type of inpaintingtechniques

In the case of texture synthesis methods the techniquedeveloped by Efros and Leung [12] uses one pixel as astarting point located on the edge of the occluded areadefining a window around it in order to find similar blocksin the region This method restores texture pixel by pixeltherefore the proposed algorithm overcomes the limitationsof Bertalmiorsquos algorithm and the similar ones

Efros and Freeman [13] present an approach in whichtexture synthesis is performed using blocks not pixel by pixelwhich significantly reduces the execution timeThe algorithmhas proven to be more efficient by copying an entire blockwhen a valid candidate is found in the source Although themethod is much faster and therefore more efficient yet itfails to provide good results for images with highly structuredtextures

Heeger and Bergen [14] proposed a texture reconstruc-tion method using a collection containing intermediateimages that form a so-called image pyramid Their methodconsists of an iterative process in which the image pyramidis created by dividing the damaged image and the onerepresenting the source According to the authors repeatingthe process for a number of steps a texture with satisfactoryresults will be obtained yet valid only for stochastic types Inthe paper of de Bonet [15] an improvement was proposed inorder to reproduce also regular textures This is achieved bytaking into account dependencies between different levels oftexture granularity Igehy and Pereira [16] describe anotherversion of the algorithm proposed by Heeger and Bergeninvolving a new step that uses a mask containing subunitvalues aiming to specify the amount of information from theoriginal image used for synthesizing the texture

The same inpainting category could include an algorithmbased on templates developed by Criminisi et al [17] Theauthors are describing a technique highlighting the impor-tance of the order in which pixels are restoredThe algorithmstarts from the edge of the occluded area assigning each pixelfrom the edge a priority Texture synthesis is donewith blocksby replicating information from a source area depending onthe priority value determined for each pixel

The algorithm proposed by Drori et al [2 18] focuseson the details of granularity levels which are used as anestimation of the best levels It then sets a filling orderby means of a confidence value followed by a search stepsimilar to Efros and Leung Their algorithm uses severaldifferent orientations of the block The inpainting algorithmof Guillemot et al [19] searches the k-nearest neighbors ofthe damage to be filled and linearly combines them in orderto replace the restored pixels The k-nearest neighbor searchis then improved by linear regression

Hays and Efros [20] present a method that uses a largeimage collection as a database for restoration The authorspoint out that the possibility to restore the region in anatural manner increases due to the amount of informationcontained in the large images set The restoration process isdone by checking each item in the database for a possiblematch of the damaged region using an image descriptor The

same approach was presented by Le Meur and Guillemot[21] introducing an exemplar-based inpainting frameworkA coarse version is first inpainted allowing reducing thecomputational complexity and noise sensitivity and extract-ing the dominant orientations of image structures A novelconcept of sparsity at the patch level is proposed by Xuand Sun [22] in order to model patch priority and patchrepresentation two important steps for patch propagationin exemplar-based inpainting Aujol et al [23] provideexperimental confirmation of the fact that exemplar-basedalgorithms could reconstruct local geometric informationwhile the minimization of variational models allows aglobal reconstruction of geometry and especially of smoothedges

One of the hybrid inpainting methods belongs toBertalmio et al [24] who developed an algorithm based onthe idea of decomposing the original image in two layers Onelayer should contain the structural characteristics and thesecond the texture The first image would be processed by astructural inpainting algorithm [4] and the second onewouldbe processed by the texture synthesis algorithm proposedby Efros and Leung in [12] The results of both operationscontribute to the final image Another hybrid method wasproposed by Atzori and de Natale [25] In this case therestoration process starts from matching the contours thatcrosses the edge of the occluded area in its interior Thisoperation will lead to smaller regions that will be filled bycopying blocks from the outside Rares et al [26] proposedthat both the local and the global information should betaken into consideration for the union of the edge contoursintersecting the damaged area Thus pairs of lines which aremore accurate will be obtained but the matching processwill be more complicated Restored pixel values are thenassigned according to the pixels in the proximity of the newcontours obtained and according to the edges of the occludedarea

2 The Inpainting Techniques Used inOur Evaluation

For the scope of our research five inpainting algorithms werechosen The first algorithm was developed by Bertalmio etal [4] and represents a reference inpainting method Thesecond presented in [9] depicts a simple solution based ona convolution operation followed by the third an adaptedversion of the previous [27] The following technique wasproposed by Efros and Leung [12] for texture synthesis andthe last considered algorithm from Criminisi et al [17]combining techniques for structured inpainting and texturereproduction

21 Bertalmiorsquos Algorithm In order to obtain the restoredimage it is necessary to interleave inpainting steps witha number of anisotropic diffusion steps Considering theoccluded area Ω and the contour of the region 120597Ω thepurpose of the method is to propagate the information alongisophote lines that crosses the contour 120597Ω [4] The algorithmoperates iteratively and creates a family of images each

The Scientific World Journal 3

image representing an improved version of the previous oneConsider

119868119899+1

(119894 119895) = 119868119899

(119894 119895) + Δ119905119868119899

119905(119894 119895) forall (119894 119895) isin Ω (1)

where 119868119899+1(119894 119895) is the intensity of the pixel having thecoordinates (119894 119895) at moment 119899 + 1 Δ119905 is the improvement orchange rate and 119868119899

119905(119894 119895) corresponds to an image update at

time 119899This update includes the information to be propagatedand the direction of propagation as follows

119868119899

119905(119894 119895) = (

997888997888rarr120575119871119899

(119894 119895) sdot (119894 119895 119899)

10038161003816100381610038161003816 (119894 119895 119899)

10038161003816100381610038161003816

)1003816100381610038161003816nabla119868119899

(119894 119895)1003816100381610038161003816 (2)

where997888997888rarr120575119871119899 is a vector indicating the intensity change in

the image obtained after applying the Laplace operator Theisophote line direction is expressed as follows

(119894 119895 119899)

10038161003816100381610038161003816 (119894 119895 119899)

10038161003816100381610038161003816

=

(minus119868119899

119910(119894 119895) 119868

119899

119909(119894 119895))

radic(119868119899119909(119894 119895))

2

+ (119868119899119910(119894 119895))

2

+ 120576

(3)

where 120576 is a small value intended to avoid potential divisionby 0 and 119868119899

119909 119868119899119910are intensities determined by the difference

between the intensities of the next pixel and the previousone The slope limited norm of the gradient has the aim ofimproving the stability1003816100381610038161003816nabla119868119899

(119894 119895)1003816100381610038161003816

=

radic(119868119899

119909119887119898)2

+ (119868119899

119909119891119872)

2

+ (119868119899

119910119887119898)

2

+ (119868119899

119910119891119872)

2

120573119899

gt 0

radic(119868119899

119909119887119872)2

+ (119868119899

119909119891119898)

2

+ (119868119899

119910119887119872)

2

+ (119868119899

119910119891119898)

2

120573119899

lt 0

120573119899

(119894 119895) =997888997888rarr120575119871119899

(119894 119895) sdot (119894 119895 119899)

10038161003816100381610038161003816 (119894 119895 119899)

10038161003816100381610038161003816

(4)

Indices 119887 and 119891 specify the difference between the intensitiesof the current pixel and the one in the reverse direction orforward on OX and OY coordinate axes Indices 119898 and 119872express the fact that the minimum or the maximum valuebetween the obtained result and 0 will be chosen

The method proposed by Bertalmio et al interleaves anumber of 119860 inpainting steps with 119861 anisotropic diffusionsteps whereA B and T (total number of iterations) are inputparameters We have used an anisotropic diffusion proposedby Perona and Malik [28] presenting a function limiting thediffusion process to homogeneous regions

119868119905+1

119894119895= 119868119905

119894119895+ 120582[119888119873sdot nabla119873119868 + 119888119878sdot nabla119878119868 + 119888119864sdot nabla119864119868 + 119888119882sdot nabla119882119868]119905

119894119895

(5)

where 119868119905+1119894119895

is the intensity of the pixel having the (119894 119895)coordinates at 119905 + 1 moment 120582 is a constant value whichshould be in the range [0 025] for algorithm stability

a a

aa

b

b b

b

0 0

c c c

c c c

c c

Figure 1 The convolution kernels proposed by Oliviera et al [9]

and nabla119873119868 nabla119878119868 nabla119864119868 nabla119882119868 represent the difference between the

intensities of the pixel in the direction indicated by the index(north south east or west) and the current pixel

nabla119873119868119894119895equiv 119868119894119895minus1

minus 119868119894119895

nabla119878119868119894119895equiv 119868119894119895+1

minus 119868119894119895

nabla119864119868119894119895equiv 119868119894+1119895

minus 119868119894119895

nabla119882119868119894119895equiv 119868119894minus1119895

minus 119868119894119895

(6)

with 119888119873 119888119878 119888119864 119888119882called conduction coefficients determined

based on the gradient There are several methods to computethese values including the following two proposed by theauthors

119888 (nabla119868) = 119890minus(nabla119868119870)

2

(7)

119888 (nabla119868) =1

1 + (nabla119868119870)2 (8)

The coefficients are determined using one of (7) or (8) wherethe gradient nabla119868 corresponding to the direction described bythe index and119870 controls the sensitivity of the edge detectionprocess Both the inpainting stage itself and the anisotropicdiffusion method will be applied to the RGB components ofthe pixel

22 Oliveirarsquos Algorithm Based on the previous methodOliviera et al [9] have proposed an inpainting algorithm thatrelies exclusively on diffusionThe processing steps consist ofdeleting color information inside the mask followed by edgedetection for the occluded area Starting from the pixels onthe edge a convolution operation is then applied using aneighborhood centered on each contour pixel and one of thekernels proposed (Figure 1) The values of a b and 119888 for bothkernels are 0073235 0176765 and 0125 respectively [9]

23 Hadhoud Moustafa and Shenodarsquos Algorithm Hadhoudet al [27] have proposed an improvement of Oliveirarsquosmethod regarding both the final image and the requiredprocessing time Some steps have been kept from the originalmethod of [9] involving the selection of the mask followedby the removal of the existing color information in the maskUnlike Oliveirarsquos algorithm the method uses a differentlydefined convolution kernel The idea was to use as much

4 The Scientific World Journal

a a

a

a

b

b b

b 00

c c c

c c

c c

c

Figure 2The convolution kernels proposed by Hadhoud et al [27]

as possible information from outside of the region in viewof the restoration process (Figure 2) By using more knownneighbors the restoration can be achieved even within asingle iteration

24 Efros and Leungrsquos Algorithm The algorithm steps includedefining a mask and specifying a source area followed bythe edge detection for the occluded area [12] All pixels onthe edge will be sorted in descending order by the numberof known neighbors A template will be defined centeredfor each pixel chosen for restoration This window has aparameterized size and it will be used in searching for similarblocks in the source area The similarity measure is given bythe sum of squared differences (SSD) To preserve the localcharacter of the texture a Gaussian kernel is used which aimsto control the influence of pixels located too far from theoccluded area Consider

119889 = 119889SSD lowast 119866 (9)

Depending on the SSD value a collection of candidate blockswill be obtained Consider

120596best = arg min120596

119889 (120596 (119901) 120596) sub 119868smp

119889 (120596 (119901) 120596) lt (1 + 120576) 119889 (120596 (119901) 120596best) 120576 = 01

(10)

where the processed pixel is119901 119868smp represents the source areaand 119889(120596(119901) 120596) describes the distance from a 120596 sized windowcentered on pixel 119901 to a block of the same size 120596 found in thesource One of the candidate blocks will be chosen randomlyand the color information of its center pixel will be assignedto the pixel on the edge (the center of the window template)

25 Criminisirsquos Algorithm This algorithm aims to achievetexture synthesis taking into consideration structural infor-mation such as the isophote lines that cross the edge of theoccluded area [17] It consists of three major steps and startswith the pixels on the edge 120597Ω of themaskΩ For all windowscentered on edge pixels a priority 119875(119901) is computed where 119901represents the processed pixel at a certain moment

119875 (119901) = 119862 (119901) lowast 119863 (119901) (11)

where 119862(119901) represents a confidence term associated with ablock 120595

119901(the higher the number of known pixels in the

window the higher the confidence) 119863(119901) is a term that

processes the structural information contained in thewindow120595119901and raises the priority of a block comprising an isophote

line These two terms are defined as follows

119862 (119901) =

sum119902isin120595119901cap(119868minusΩ)

119862 (119902)

10038161003816100381610038161003816120595119901

10038161003816100381610038161003816

119863 (119901) =

10038161003816100381610038161003816nabla119868perp

119901119899119901

10038161003816100381610038161003816

120572

(12)

with |120595119901| the surface of the window 120595

119901centered in pixel 119901

belonging to 120597Ω where 120572 is a normalization factor with value255 119899

119901is the normal to the contour 120597Ω at point 119901 and nabla119868perp

119901is

the normal to the gradient namely the isophote lineFor the priorities 119875(119901) an initialization step is required

All pixels belonging to the mask have the confidence term119862(119901) = 0 and the ones belonging to the source band havethe confidence 119862(119901) = 1

The second processing step represented the inpaintingitself The pixel having the highest priority is the first to beprocessed its associated source block from the source area isthe one that leads to a minimal SSD distance

120595119902= arg min120595119902isinΦ

119889 (120595119901 120595119902) (13)

where 119889(120595119901 120595119902) represents the SSD value (between all known

pixels of the window 120595119901and the ones on the corresponding

positions in a block 120595119902belonging to the source band)

Knowing the source window 120595119902 all pixels of 120595

119901that also

belong to the mask will be filled with information providedby the corresponding pixels in 120595

119902 The last step consists of

updating the confidence values associated with pixels in therestored window

119862 (119901) = 119862 (119901) forall119901 isin 120595119901cap Ω (14)

3 A Proposed Adaptation of Oliveirarsquos andHadhoudrsquos Algorithms

Concerning the algorithm developed by Oliveira and itsadaptation proposed byHadhoud et al [27] conserving edgesis one of themajor problemsTherefore Oliveira et al definedsome diffusion barriers over the contour in order to stop theisotropic diffusion process otherwise some visible blurringeffects may occur However in the case of Hadhoud et alredefining the kernel and the direction of propagation leads toevenmore highlighted blurring effects and the loss of contourlines

As an alternative to the 2-pixel width barriers definedaccording to Oliveirarsquos idea we are proposing an edgeconserving procedure by defining an additional mask thatcomprises the contour The mask will be processed usingan anisotropic diffusion operation described in Bertalmiorsquosalgorithm The mask pixels are excluded from the initialmask and will no longer be modified using one of thekernels of isotropic smoothing operation As a result the userintervention is simplified and the results are satisfactory

Oliveirarsquos and Hadhoudrsquos methods are suited for imageswith natural defects such as Lincoln Unfortunately the

The Scientific World Journal 5

(a) (b)

(c) (d)

(e) (f)

Figure 3 Visual comparison of the proposed methods (a) simulation of a natural defect (b) the corresponding masks (c) result of theOliveira method (d) result of the proposed adaptation of Oliveira method (e) result of the Hadhoud method (f) result of the proposedadaptation of Hadhoud method

original image (without defects) does not exist thereforewe could not compute the PSNR in comparison to it Inorder to reach a conclusion regarding these methods and ourproposal for edge preserving some images were chosen anddefects were manually appliedTherefore the PSNR could becomputed by comparing the restored image with the originalone

In the image shown in Figure 3(a) we have applied adefect that could be considered close to a natural one Theblue mask will be processed using Oliveirarsquos or Hadhoudrsquosmethod as for the yellow mask an anisotropic diffusionwill be applied It can be noticed from the result in Figure 3and Table 1 that our proposal offers improvements regardingHadhoudrsquos method However it worth mentioning that theresults would be more relevant if images with natural defectswould have been tested and their originals could be used as aground truth

Table 1 PSNR values comparison for the proposed methods

Image Oliveira Hadhoud Our adaptation ofOliveira

Our adaptation ofHadhoud

Peppers 47155 42383 467605 43138Egipt 463948 438093 46038 4600067

4 An Evaluation of the Inpainting Algorithms

The five inpainting methods were implemented in the Cand run on a system with Intel i5 processor at 25 GHz Themethod proposed by Bertalmio et al was implemented onRGB color images The algorithm developed by Oliveira etal and the method proposed by Hadhoud et al were imple-mented taking into consideration the proposal describedabove regarding edge conservation In the case of Efros and

6 The Scientific World Journal

30

35

40

45

50

849 1348 1806 2636

Bertalmio et al (2000 2003)Oliviera et al (2001)Hadhoud et al (2009)

Efros and Leung (1999)Criminisi et al (2004)

Mask size

PSN

R

(a)

849 1348 1806 2636Mask size

PSN

R

30354045505560

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 4 PSNR results for (a) Lena and (b) Peepers test image

30

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

30

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

25

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 5 PSNR results for (a) Baboon and (b) StillLifeWithApples test image

Leungrsquos algorithm the source area was represented by aband around the occluded region [2] The same assumptionwas considered for the method proposed by Criminisi etal Our evaluation was carried out on representative testimages characterized by structural lines but also by texturecontent

First of all it was necessary to determine the optimalconfiguration of each method parameters in order to obtainthe best results in terms of PSNR Therefore several con-figurations for each algorithm were tested The test imagesused were Lena Peppers Baboon and StillLifeWithApplesas presented in [17] and Barbara Egipt cat fur fly helicopterand lands from [29] An artificial damage was applied and therestored image was compared to the original one as referenceOliveirarsquos method and the version proposed by Hadhoudet al were tested on the well-known inpainting test imagesLincoln and Three Girls due to their efficiency on naturaldamage images The main disadvantage was that there areno original images that could be used as reference in orderto compute the PSNR value Our artificial test damage wasdefined as a stripe successively widened in order to noticehow the algorithm behaves for ldquospot masksrdquo The data inTable 2 presents the mask (damage) size in pixels and thecorresponding initial PSNR values By gradually increasing

the mask width we had obtained the PSNR results presentedin Figures 4 5 6 7 and 8 for the ten considered test images

As it can be seen from the PSNR results among the struc-tural inpainting methods the one belonging to Bertalmioleads to the successful results among which Peppers andLena obtain the highest values Due to diffusion method thealgorithm has lower results for textural images in comparisonwith structural ones

For the last two methods there are some improvementsbut it is important to mention that in the case of texturalimages the PSNR value is not relevant as inpainting is per-formed by the replication of information from a source areaand not by actual propagation inside themask Consequentlyas the mask increases it is likely to obtain lower PSNR valuesand still have a very successful visual effect (as it can beseen from Figure 9) In the case of diffusion methods theresults are less successful leading to color spread and causingblurring effects

Considering the proposed adaptation for contour linepreserving of Oliveirarsquos and Hadhoudrsquos methods described inSection 3 an improvement has been noticed in comparisonto the basic algorithm which applied isotropic diffusion overthe entire mask Unfortunately since these two methods aresuitable for natural defects images they cannot be compared

The Scientific World Journal 7

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

55

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 6 PSNR results for (a) Barbara and (b) Egipt test image

35

40

849 1348 1806 2636Mask size

PSN

R

30

25

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

25

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 7 PSNR results for (a) cat fur and (b) fly test image

Table 2 The initial PSNR values depending on the mask size

Image Mask size (pixels)849 1348 1806 2636

Lena 3726898 349823 330214 3023914Peppers 3461765 321941 304215 278131Baboon 327125 301962 2843159 257326StillLifeWithApples 329604 307468 289399 263172Barbara 3343815 3112731 29333 266095Egipt 2994907 275565 2572072 230565Cat fur 3210376 2958541 2761433 251814Fly 30935 28547 2670904 239389Helicopter 353284 330786 3132791 287093Lands 303642 280075 262049 234996

to an original (unaltered) image In this case the PSNRvalue would be computed in comparison with other restoredimages from the literature indicating the similarity to themand the obtained values would not be a proof of a successfulrestoration There are no original images for Lincoln andThree Girls (highly referenced in the domain) therefore

a conclusive PSNR value could be determined and only avisual analysis would be possible

However the visual restoration is satisfactory as it canbe seen from Figures 10(c) and 10(e) and is processed usingour proposedmethod for edge preserving applied toOliveirarsquosand Hadhoudrsquos methods respectively In comparison withthe original Oliveira method where the obtained edge wasblurred (as shown in Figure 10(b)) our approach offers bettercontour preservation (Figure 10(c)) Also due to the kernelused in Hadhoudrsquos method the edge is altered (Figure 10(d))However applying the proposedmethod in combinationwithHadhoudrsquos leads to good visual results (Figure 10(e)) We willconclude that using our new procedure in combination withOliveirarsquos and Hadhoudrsquos methods will offer advantages inthe case of natural defects images such as Lincoln and ThreeGirls

It was found that the algorithm proposed by Bertalmioet al successfully restores images when the method isapplied to reduced surface masks or with narrow widthbecause the contour lines crossing the area can be properlyconnected The major disadvantage of the algorithm is thatfor large masks due to diffusion a blurring effect occursand therefore the algorithm fails to restore textural images

8 The Scientific World Journal

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

35404550

849 1348 1806 2636Mask size

PSN

R

30

70656055

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 8 PSNR results for (a) helicopter and (b) lands test image

(a) (b)

(c) (d)

Figure 9 Test images with damage of (a) 983 pixels (c) 2120 pixels and the corresponding results (b) and (d) obtained with Criminisirsquosalgorithm

The method however can lead to good results using smallamount of information around the mask unlike the textureinpainting algorithms which requires a more significantamount of information in order to perform the restoration

Unlike the algorithm proposed by Bertalmio et al themethod presented by Oliveira et al is less complex Howeverthis advantage fails to compensate the fact that the contourlines can be preserved only by defining the diffusion barriersand the algorithm can be successfully applied to images withnatural damageTherefore the algorithm is suitable formaskshaving narrow width otherwise a high blurring effect can benoticed

In the case of Hadhoud et al method processing timeimprovements could be noticed as a consequence of thefact that more known neighbors of the restoring pixel areused Hence the required number of iterations considerablydecreases Similarly to the Oliveira et al method the algo-rithm is suitable for restoring images that do not have highcontrast

The texture synthesis algorithm proposed by Efros andLeung led to impressive results Although in contrast toother methods the numerical values may be less satisfactorybecause the stochastic textures would be impossible torestore The restored pixels have been assigned a close value

The Scientific World Journal 9

(a) (b)

(c) (d)

(e)

Figure 10 (a) Original image (b) result for Oliveirarsquos method (c) result for our adaptation of Oliveirarsquos method (d) result for Hadhoudrsquosmethod (e) result for our adaptation of Hadhoudrsquos method

to the original one as inpainting is done by copying pixelsfromapredetermined area andnot by propagation of externalinformation The method performs well also for structuralimages but the main disadvantage consists of the extremelylong processing time caused by the pixel by pixel restoration

The Criminisi method leads to good results both forstructural and textural images since it takes into consid-eration structural information Unlike the Efros and Leungalgorithm restoration is performed block by block reducingthe processing time As a consequence a disadvantage may

occur when choosing too large blocks for replication asinappropriate information can be copied inside the occludedarea The quality of the results heavily depends on thisparameter but also on the provided context by means of asecond parameter which specifies the source bandwidth

5 Conclusions

The paper presents a comparative study regarding inpaintingtechniques in order to evaluate different types of image

10 The Scientific World Journal

restoration methods and to emphasize the advantages anddisadvantages for each of the approached algorithms

Due to the fact that a certain number of inpaintingmethods have been proposed during the last years it is stilldifficult to designate the appropriate one The algorithmschosen for our evaluation are representative for the categoriesthey belong to having as reference the first one developedby Bertalmio Other methods were also analyzed as theone proposed by Oliveira and its adapted version proposedby Hadhoud et al suitable for images without texturesRegarding these two methods an alternative to the diffusionbarriers was proposed by us The restoration of texturedimages had also been taken into account in our evaluationby using the method developed by Efros and Leung and thealgorithm proposed by Criminisi

It was also important to determine the algorithm param-eters that lead to the best PSNR results and selectingrepresentative test images to provide relevant informationThe images were restored gradually varying the width ofthe occluded area in order to analyze the influence of thisparameter The tests have shown that inpainting algorithmsinvolving diffusion operations perform well for structuralfeatures images but cannot successfully rebuild textures

Image restoration using the RGB color system for thealgorithm developed by Bertalmio led to successful resultsfor structural images The adaptation proposed for Oliveirarsquosand Hadhoudrsquos algorithms has been proven to be a successfulalternative for edge preserving with remarkable resultsHowever textural inpainting techniques are themost success-ful Even if requiring a longer processing time they performwell on both image types

Further developments of this work may consist ofimplementing hybrid methods that combine features of theapproached algorithms and comparing their results with theones belonging to the already analyzed methods Hybridmethods would require reconstruction processes for thecontour lines and restoration processes over the obtainedregions by means of textural inpainting techniques

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] P Patel A Prajapati and SMishra ldquoReviewof different inpaint-ing algorithmsrdquo International Journal of Computer Applicationsvol 59 no 18 pp 30ndash34 2012

[2] M E Taschler ldquoA comparative analysis of image inpaintingrdquoTech Rep University of York York UK 2006

[3] C Guillemot and O Le Meur ldquoImage inpainting overview andrecent advancesrdquo IEEE Signal Processing Magazine vol 31 pp127ndash144 2014

[4] M Bertalmio G Sapiro V Caselles and C Ballester ldquoImageinpaintingrdquo in Proceedings of the 27th annual conference onComputer graphics and interactive techniques (SIGGRAPH rsquo00)pp 417ndash424 July 2000

[5] A BugeauM Bertalmio V Caselles and G Sapiro ldquoA compre-hensive framework for image inpaintingrdquo IEEE Transactions onImage Processing vol 19 no 10 pp 2634ndash2645 2010

[6] T F Chan and J Shen ldquoNontexture inpainting by curvature-driven diffusionsrdquo Journal of Visual Communication and ImageRepresentation vol 12 no 4 pp 436ndash449 2001

[7] D Tschumperle and R Deriche ldquoVector-valued image regular-ization with PDEs a common framework for different appli-cationsrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 27 no 4 pp 506ndash517 2005

[8] J Sun L Yuan J Jia and H Y Shum ldquoImage completion withstructure propagationrdquo ACM Transactions on Graphics vol 24pp 861ndash868 2005

[9] M M Oliviera B Bowen R McKenna and Y S Chang ldquoFastdigital image inpaintingrdquo in Proceedings of the InternationalConference on Visualization Imaging and Image Processing(VIIP rsquo01) pp 261ndash266 2001

[10] A Telea ldquoAn image inpainting technique based on the fastmarching methodrdquo Journal of Graphics Tools vol 9 pp 23ndash342004

[11] B Yan Y Gao K Sun and B Yang ldquoEfficient seam carvingfor object removalrdquo in Proceedings of the 20th IEEE Interna-tional Conference on Image Processing (ICIP rsquo13) pp 1331ndash1335September 2013

[12] A A Efros and T K Leung ldquoTexture synthesis by non-parametric samplingrdquo in Proceedings of the 7th IEEE Interna-tional Conference onComputer Vision (ICCVrsquo99) pp 1033ndash1038Corfu Greece September 1999

[13] A A Efros and W T Freeman ldquoImage quilting for texturesynthesis and transferrdquo in Proceedings of the 28th AnnualConference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo01) pp 341ndash346 Los Angeles Calif USA August2001

[14] D J Heeger and J R Bergen ldquoPyramid-based texture anal-ysissynthesisrdquo in Proceedings of the 22nd Annual ACM Con-ference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo95) vol 29 pp 229ndash238 Los Angeles Calif USAAugust 1995

[15] J S de Bonet ldquoMultiresolution sampling procedure for analysisand synthesis of texture imagesrdquo in Proceedings of the 24thAnnual Conference on Computer Graphics and Interactive Tech-niques (SIGGRAPH rsquo97) pp 361ndash368 Los Angeles Calif USAAugust 1997

[16] H Igehy and L Pereira ldquoImage replacement through texturesynthesisrdquo in Proceedings of the International Conference onImage Processing vol 3 pp 186ndash189 Santa Barbara Calif USAOctober 1997

[17] A Criminisi P Perez and K Toyama ldquoRegion filling andobject removal by exemplar-based image inpaintingrdquo IEEETransactions on Image Processing vol 13 no 9 pp 1200ndash12122004

[18] I Drori D Cohen-Or and H Yeshurun ldquoFragmentmdashbasedimage completionrdquo ACM Transactions on Graphics vol 22 pp303ndash312 2003

[19] C Guillemot M Turkan O L Meur and M Ebdelli ldquoImageinpainting using LLE-LDNR and linear subspace mappingsrdquo inProceedings of the IEEE International Conference on AcousticsSpeech and Signal Processing (ICASSP rsquo13) pp 1558ndash1562 May2013

[20] J Hays and A Efros ldquoScene completion using millions ofphotographsrdquo ACM Transactions on Graphics (SIGGRAPH2007) vol 26 no 3 2007

The Scientific World Journal 11

[21] O LeMeur and C Guillemot ldquoSuper-resolution-based inpaint-ingrdquo in Proceedings of European Conference on Computer Vision(ECCV rsquo12) pp 554ndash567 2012

[22] Z Xu and J Sun ldquoImage inpainting by patch propagation usingpatch sparsityrdquo IEEE Transactions on Image Processing vol 19no 5 pp 1153ndash1165 2010

[23] J Aujol S Ladjal and S Masnou ldquoExemplar-based inpaintingfrom a variational point of viewrdquo SIAM Journal onMathematicalAnalysis vol 42 no 3 pp 1246ndash1285 2010

[24] M Bertalmio L Vese G Sapiro and S Osher ldquoSimultaneousstructure and texture image inpaintingrdquo IEEE Transactions onImage Processing vol 12 no 8 pp 882ndash889 2003

[25] L Atzori and F G B de Natale ldquoError concealment in videotransmission over packet networks by a sketch-based approachrdquoSignal Processing ImageCommunication vol 15 no 1 pp 57ndash761999

[26] A Rares M J T Reinders and J Biemond ldquoEdge-based imagerestorationrdquo IEEE Transactions on Image Processing vol 14 no10 pp 1454ndash1468 2005

[27] M M Hadhoud K A Moustafa and S Z Shenoda ldquoDigitalimages inpainting using modified convolution based methodrdquoinOptical Pattern Recognition XX vol 7340 of Proceedings of theSPIE Orlando Fla USA April 2009

[28] P Perona and J Malik ldquoScale-space and edge detection usinganisotropic diffusionrdquo IEEE Transactions on Pattern Analysisand Machine Intelligence vol 12 no 7 pp 629ndash639 1990

[29] M Daisy D Tschumperle and O Lezoray ldquoA fast spatialpatch blending algorithm for artefact reduction in pattern-based image inpaintingrdquo in SIGGRAPH Asia 2013 TechnicalBriefs (SA rsquo13) pp 1ndash4 article 8 ACM New York NY USA2013

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DistributedSensor Networks

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2 The Scientific World Journal

A seam carving method was presented in [11] overcomingthe time consuming disadvantage of this type of inpaintingtechniques

In the case of texture synthesis methods the techniquedeveloped by Efros and Leung [12] uses one pixel as astarting point located on the edge of the occluded areadefining a window around it in order to find similar blocksin the region This method restores texture pixel by pixeltherefore the proposed algorithm overcomes the limitationsof Bertalmiorsquos algorithm and the similar ones

Efros and Freeman [13] present an approach in whichtexture synthesis is performed using blocks not pixel by pixelwhich significantly reduces the execution timeThe algorithmhas proven to be more efficient by copying an entire blockwhen a valid candidate is found in the source Although themethod is much faster and therefore more efficient yet itfails to provide good results for images with highly structuredtextures

Heeger and Bergen [14] proposed a texture reconstruc-tion method using a collection containing intermediateimages that form a so-called image pyramid Their methodconsists of an iterative process in which the image pyramidis created by dividing the damaged image and the onerepresenting the source According to the authors repeatingthe process for a number of steps a texture with satisfactoryresults will be obtained yet valid only for stochastic types Inthe paper of de Bonet [15] an improvement was proposed inorder to reproduce also regular textures This is achieved bytaking into account dependencies between different levels oftexture granularity Igehy and Pereira [16] describe anotherversion of the algorithm proposed by Heeger and Bergeninvolving a new step that uses a mask containing subunitvalues aiming to specify the amount of information from theoriginal image used for synthesizing the texture

The same inpainting category could include an algorithmbased on templates developed by Criminisi et al [17] Theauthors are describing a technique highlighting the impor-tance of the order in which pixels are restoredThe algorithmstarts from the edge of the occluded area assigning each pixelfrom the edge a priority Texture synthesis is donewith blocksby replicating information from a source area depending onthe priority value determined for each pixel

The algorithm proposed by Drori et al [2 18] focuseson the details of granularity levels which are used as anestimation of the best levels It then sets a filling orderby means of a confidence value followed by a search stepsimilar to Efros and Leung Their algorithm uses severaldifferent orientations of the block The inpainting algorithmof Guillemot et al [19] searches the k-nearest neighbors ofthe damage to be filled and linearly combines them in orderto replace the restored pixels The k-nearest neighbor searchis then improved by linear regression

Hays and Efros [20] present a method that uses a largeimage collection as a database for restoration The authorspoint out that the possibility to restore the region in anatural manner increases due to the amount of informationcontained in the large images set The restoration process isdone by checking each item in the database for a possiblematch of the damaged region using an image descriptor The

same approach was presented by Le Meur and Guillemot[21] introducing an exemplar-based inpainting frameworkA coarse version is first inpainted allowing reducing thecomputational complexity and noise sensitivity and extract-ing the dominant orientations of image structures A novelconcept of sparsity at the patch level is proposed by Xuand Sun [22] in order to model patch priority and patchrepresentation two important steps for patch propagationin exemplar-based inpainting Aujol et al [23] provideexperimental confirmation of the fact that exemplar-basedalgorithms could reconstruct local geometric informationwhile the minimization of variational models allows aglobal reconstruction of geometry and especially of smoothedges

One of the hybrid inpainting methods belongs toBertalmio et al [24] who developed an algorithm based onthe idea of decomposing the original image in two layers Onelayer should contain the structural characteristics and thesecond the texture The first image would be processed by astructural inpainting algorithm [4] and the second onewouldbe processed by the texture synthesis algorithm proposedby Efros and Leung in [12] The results of both operationscontribute to the final image Another hybrid method wasproposed by Atzori and de Natale [25] In this case therestoration process starts from matching the contours thatcrosses the edge of the occluded area in its interior Thisoperation will lead to smaller regions that will be filled bycopying blocks from the outside Rares et al [26] proposedthat both the local and the global information should betaken into consideration for the union of the edge contoursintersecting the damaged area Thus pairs of lines which aremore accurate will be obtained but the matching processwill be more complicated Restored pixel values are thenassigned according to the pixels in the proximity of the newcontours obtained and according to the edges of the occludedarea

2 The Inpainting Techniques Used inOur Evaluation

For the scope of our research five inpainting algorithms werechosen The first algorithm was developed by Bertalmio etal [4] and represents a reference inpainting method Thesecond presented in [9] depicts a simple solution based ona convolution operation followed by the third an adaptedversion of the previous [27] The following technique wasproposed by Efros and Leung [12] for texture synthesis andthe last considered algorithm from Criminisi et al [17]combining techniques for structured inpainting and texturereproduction

21 Bertalmiorsquos Algorithm In order to obtain the restoredimage it is necessary to interleave inpainting steps witha number of anisotropic diffusion steps Considering theoccluded area Ω and the contour of the region 120597Ω thepurpose of the method is to propagate the information alongisophote lines that crosses the contour 120597Ω [4] The algorithmoperates iteratively and creates a family of images each

The Scientific World Journal 3

image representing an improved version of the previous oneConsider

119868119899+1

(119894 119895) = 119868119899

(119894 119895) + Δ119905119868119899

119905(119894 119895) forall (119894 119895) isin Ω (1)

where 119868119899+1(119894 119895) is the intensity of the pixel having thecoordinates (119894 119895) at moment 119899 + 1 Δ119905 is the improvement orchange rate and 119868119899

119905(119894 119895) corresponds to an image update at

time 119899This update includes the information to be propagatedand the direction of propagation as follows

119868119899

119905(119894 119895) = (

997888997888rarr120575119871119899

(119894 119895) sdot (119894 119895 119899)

10038161003816100381610038161003816 (119894 119895 119899)

10038161003816100381610038161003816

)1003816100381610038161003816nabla119868119899

(119894 119895)1003816100381610038161003816 (2)

where997888997888rarr120575119871119899 is a vector indicating the intensity change in

the image obtained after applying the Laplace operator Theisophote line direction is expressed as follows

(119894 119895 119899)

10038161003816100381610038161003816 (119894 119895 119899)

10038161003816100381610038161003816

=

(minus119868119899

119910(119894 119895) 119868

119899

119909(119894 119895))

radic(119868119899119909(119894 119895))

2

+ (119868119899119910(119894 119895))

2

+ 120576

(3)

where 120576 is a small value intended to avoid potential divisionby 0 and 119868119899

119909 119868119899119910are intensities determined by the difference

between the intensities of the next pixel and the previousone The slope limited norm of the gradient has the aim ofimproving the stability1003816100381610038161003816nabla119868119899

(119894 119895)1003816100381610038161003816

=

radic(119868119899

119909119887119898)2

+ (119868119899

119909119891119872)

2

+ (119868119899

119910119887119898)

2

+ (119868119899

119910119891119872)

2

120573119899

gt 0

radic(119868119899

119909119887119872)2

+ (119868119899

119909119891119898)

2

+ (119868119899

119910119887119872)

2

+ (119868119899

119910119891119898)

2

120573119899

lt 0

120573119899

(119894 119895) =997888997888rarr120575119871119899

(119894 119895) sdot (119894 119895 119899)

10038161003816100381610038161003816 (119894 119895 119899)

10038161003816100381610038161003816

(4)

Indices 119887 and 119891 specify the difference between the intensitiesof the current pixel and the one in the reverse direction orforward on OX and OY coordinate axes Indices 119898 and 119872express the fact that the minimum or the maximum valuebetween the obtained result and 0 will be chosen

The method proposed by Bertalmio et al interleaves anumber of 119860 inpainting steps with 119861 anisotropic diffusionsteps whereA B and T (total number of iterations) are inputparameters We have used an anisotropic diffusion proposedby Perona and Malik [28] presenting a function limiting thediffusion process to homogeneous regions

119868119905+1

119894119895= 119868119905

119894119895+ 120582[119888119873sdot nabla119873119868 + 119888119878sdot nabla119878119868 + 119888119864sdot nabla119864119868 + 119888119882sdot nabla119882119868]119905

119894119895

(5)

where 119868119905+1119894119895

is the intensity of the pixel having the (119894 119895)coordinates at 119905 + 1 moment 120582 is a constant value whichshould be in the range [0 025] for algorithm stability

a a

aa

b

b b

b

0 0

c c c

c c c

c c

Figure 1 The convolution kernels proposed by Oliviera et al [9]

and nabla119873119868 nabla119878119868 nabla119864119868 nabla119882119868 represent the difference between the

intensities of the pixel in the direction indicated by the index(north south east or west) and the current pixel

nabla119873119868119894119895equiv 119868119894119895minus1

minus 119868119894119895

nabla119878119868119894119895equiv 119868119894119895+1

minus 119868119894119895

nabla119864119868119894119895equiv 119868119894+1119895

minus 119868119894119895

nabla119882119868119894119895equiv 119868119894minus1119895

minus 119868119894119895

(6)

with 119888119873 119888119878 119888119864 119888119882called conduction coefficients determined

based on the gradient There are several methods to computethese values including the following two proposed by theauthors

119888 (nabla119868) = 119890minus(nabla119868119870)

2

(7)

119888 (nabla119868) =1

1 + (nabla119868119870)2 (8)

The coefficients are determined using one of (7) or (8) wherethe gradient nabla119868 corresponding to the direction described bythe index and119870 controls the sensitivity of the edge detectionprocess Both the inpainting stage itself and the anisotropicdiffusion method will be applied to the RGB components ofthe pixel

22 Oliveirarsquos Algorithm Based on the previous methodOliviera et al [9] have proposed an inpainting algorithm thatrelies exclusively on diffusionThe processing steps consist ofdeleting color information inside the mask followed by edgedetection for the occluded area Starting from the pixels onthe edge a convolution operation is then applied using aneighborhood centered on each contour pixel and one of thekernels proposed (Figure 1) The values of a b and 119888 for bothkernels are 0073235 0176765 and 0125 respectively [9]

23 Hadhoud Moustafa and Shenodarsquos Algorithm Hadhoudet al [27] have proposed an improvement of Oliveirarsquosmethod regarding both the final image and the requiredprocessing time Some steps have been kept from the originalmethod of [9] involving the selection of the mask followedby the removal of the existing color information in the maskUnlike Oliveirarsquos algorithm the method uses a differentlydefined convolution kernel The idea was to use as much

4 The Scientific World Journal

a a

a

a

b

b b

b 00

c c c

c c

c c

c

Figure 2The convolution kernels proposed by Hadhoud et al [27]

as possible information from outside of the region in viewof the restoration process (Figure 2) By using more knownneighbors the restoration can be achieved even within asingle iteration

24 Efros and Leungrsquos Algorithm The algorithm steps includedefining a mask and specifying a source area followed bythe edge detection for the occluded area [12] All pixels onthe edge will be sorted in descending order by the numberof known neighbors A template will be defined centeredfor each pixel chosen for restoration This window has aparameterized size and it will be used in searching for similarblocks in the source area The similarity measure is given bythe sum of squared differences (SSD) To preserve the localcharacter of the texture a Gaussian kernel is used which aimsto control the influence of pixels located too far from theoccluded area Consider

119889 = 119889SSD lowast 119866 (9)

Depending on the SSD value a collection of candidate blockswill be obtained Consider

120596best = arg min120596

119889 (120596 (119901) 120596) sub 119868smp

119889 (120596 (119901) 120596) lt (1 + 120576) 119889 (120596 (119901) 120596best) 120576 = 01

(10)

where the processed pixel is119901 119868smp represents the source areaand 119889(120596(119901) 120596) describes the distance from a 120596 sized windowcentered on pixel 119901 to a block of the same size 120596 found in thesource One of the candidate blocks will be chosen randomlyand the color information of its center pixel will be assignedto the pixel on the edge (the center of the window template)

25 Criminisirsquos Algorithm This algorithm aims to achievetexture synthesis taking into consideration structural infor-mation such as the isophote lines that cross the edge of theoccluded area [17] It consists of three major steps and startswith the pixels on the edge 120597Ω of themaskΩ For all windowscentered on edge pixels a priority 119875(119901) is computed where 119901represents the processed pixel at a certain moment

119875 (119901) = 119862 (119901) lowast 119863 (119901) (11)

where 119862(119901) represents a confidence term associated with ablock 120595

119901(the higher the number of known pixels in the

window the higher the confidence) 119863(119901) is a term that

processes the structural information contained in thewindow120595119901and raises the priority of a block comprising an isophote

line These two terms are defined as follows

119862 (119901) =

sum119902isin120595119901cap(119868minusΩ)

119862 (119902)

10038161003816100381610038161003816120595119901

10038161003816100381610038161003816

119863 (119901) =

10038161003816100381610038161003816nabla119868perp

119901119899119901

10038161003816100381610038161003816

120572

(12)

with |120595119901| the surface of the window 120595

119901centered in pixel 119901

belonging to 120597Ω where 120572 is a normalization factor with value255 119899

119901is the normal to the contour 120597Ω at point 119901 and nabla119868perp

119901is

the normal to the gradient namely the isophote lineFor the priorities 119875(119901) an initialization step is required

All pixels belonging to the mask have the confidence term119862(119901) = 0 and the ones belonging to the source band havethe confidence 119862(119901) = 1

The second processing step represented the inpaintingitself The pixel having the highest priority is the first to beprocessed its associated source block from the source area isthe one that leads to a minimal SSD distance

120595119902= arg min120595119902isinΦ

119889 (120595119901 120595119902) (13)

where 119889(120595119901 120595119902) represents the SSD value (between all known

pixels of the window 120595119901and the ones on the corresponding

positions in a block 120595119902belonging to the source band)

Knowing the source window 120595119902 all pixels of 120595

119901that also

belong to the mask will be filled with information providedby the corresponding pixels in 120595

119902 The last step consists of

updating the confidence values associated with pixels in therestored window

119862 (119901) = 119862 (119901) forall119901 isin 120595119901cap Ω (14)

3 A Proposed Adaptation of Oliveirarsquos andHadhoudrsquos Algorithms

Concerning the algorithm developed by Oliveira and itsadaptation proposed byHadhoud et al [27] conserving edgesis one of themajor problemsTherefore Oliveira et al definedsome diffusion barriers over the contour in order to stop theisotropic diffusion process otherwise some visible blurringeffects may occur However in the case of Hadhoud et alredefining the kernel and the direction of propagation leads toevenmore highlighted blurring effects and the loss of contourlines

As an alternative to the 2-pixel width barriers definedaccording to Oliveirarsquos idea we are proposing an edgeconserving procedure by defining an additional mask thatcomprises the contour The mask will be processed usingan anisotropic diffusion operation described in Bertalmiorsquosalgorithm The mask pixels are excluded from the initialmask and will no longer be modified using one of thekernels of isotropic smoothing operation As a result the userintervention is simplified and the results are satisfactory

Oliveirarsquos and Hadhoudrsquos methods are suited for imageswith natural defects such as Lincoln Unfortunately the

The Scientific World Journal 5

(a) (b)

(c) (d)

(e) (f)

Figure 3 Visual comparison of the proposed methods (a) simulation of a natural defect (b) the corresponding masks (c) result of theOliveira method (d) result of the proposed adaptation of Oliveira method (e) result of the Hadhoud method (f) result of the proposedadaptation of Hadhoud method

original image (without defects) does not exist thereforewe could not compute the PSNR in comparison to it Inorder to reach a conclusion regarding these methods and ourproposal for edge preserving some images were chosen anddefects were manually appliedTherefore the PSNR could becomputed by comparing the restored image with the originalone

In the image shown in Figure 3(a) we have applied adefect that could be considered close to a natural one Theblue mask will be processed using Oliveirarsquos or Hadhoudrsquosmethod as for the yellow mask an anisotropic diffusionwill be applied It can be noticed from the result in Figure 3and Table 1 that our proposal offers improvements regardingHadhoudrsquos method However it worth mentioning that theresults would be more relevant if images with natural defectswould have been tested and their originals could be used as aground truth

Table 1 PSNR values comparison for the proposed methods

Image Oliveira Hadhoud Our adaptation ofOliveira

Our adaptation ofHadhoud

Peppers 47155 42383 467605 43138Egipt 463948 438093 46038 4600067

4 An Evaluation of the Inpainting Algorithms

The five inpainting methods were implemented in the Cand run on a system with Intel i5 processor at 25 GHz Themethod proposed by Bertalmio et al was implemented onRGB color images The algorithm developed by Oliveira etal and the method proposed by Hadhoud et al were imple-mented taking into consideration the proposal describedabove regarding edge conservation In the case of Efros and

6 The Scientific World Journal

30

35

40

45

50

849 1348 1806 2636

Bertalmio et al (2000 2003)Oliviera et al (2001)Hadhoud et al (2009)

Efros and Leung (1999)Criminisi et al (2004)

Mask size

PSN

R

(a)

849 1348 1806 2636Mask size

PSN

R

30354045505560

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 4 PSNR results for (a) Lena and (b) Peepers test image

30

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

30

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

25

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 5 PSNR results for (a) Baboon and (b) StillLifeWithApples test image

Leungrsquos algorithm the source area was represented by aband around the occluded region [2] The same assumptionwas considered for the method proposed by Criminisi etal Our evaluation was carried out on representative testimages characterized by structural lines but also by texturecontent

First of all it was necessary to determine the optimalconfiguration of each method parameters in order to obtainthe best results in terms of PSNR Therefore several con-figurations for each algorithm were tested The test imagesused were Lena Peppers Baboon and StillLifeWithApplesas presented in [17] and Barbara Egipt cat fur fly helicopterand lands from [29] An artificial damage was applied and therestored image was compared to the original one as referenceOliveirarsquos method and the version proposed by Hadhoudet al were tested on the well-known inpainting test imagesLincoln and Three Girls due to their efficiency on naturaldamage images The main disadvantage was that there areno original images that could be used as reference in orderto compute the PSNR value Our artificial test damage wasdefined as a stripe successively widened in order to noticehow the algorithm behaves for ldquospot masksrdquo The data inTable 2 presents the mask (damage) size in pixels and thecorresponding initial PSNR values By gradually increasing

the mask width we had obtained the PSNR results presentedin Figures 4 5 6 7 and 8 for the ten considered test images

As it can be seen from the PSNR results among the struc-tural inpainting methods the one belonging to Bertalmioleads to the successful results among which Peppers andLena obtain the highest values Due to diffusion method thealgorithm has lower results for textural images in comparisonwith structural ones

For the last two methods there are some improvementsbut it is important to mention that in the case of texturalimages the PSNR value is not relevant as inpainting is per-formed by the replication of information from a source areaand not by actual propagation inside themask Consequentlyas the mask increases it is likely to obtain lower PSNR valuesand still have a very successful visual effect (as it can beseen from Figure 9) In the case of diffusion methods theresults are less successful leading to color spread and causingblurring effects

Considering the proposed adaptation for contour linepreserving of Oliveirarsquos and Hadhoudrsquos methods described inSection 3 an improvement has been noticed in comparisonto the basic algorithm which applied isotropic diffusion overthe entire mask Unfortunately since these two methods aresuitable for natural defects images they cannot be compared

The Scientific World Journal 7

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

55

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 6 PSNR results for (a) Barbara and (b) Egipt test image

35

40

849 1348 1806 2636Mask size

PSN

R

30

25

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

25

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 7 PSNR results for (a) cat fur and (b) fly test image

Table 2 The initial PSNR values depending on the mask size

Image Mask size (pixels)849 1348 1806 2636

Lena 3726898 349823 330214 3023914Peppers 3461765 321941 304215 278131Baboon 327125 301962 2843159 257326StillLifeWithApples 329604 307468 289399 263172Barbara 3343815 3112731 29333 266095Egipt 2994907 275565 2572072 230565Cat fur 3210376 2958541 2761433 251814Fly 30935 28547 2670904 239389Helicopter 353284 330786 3132791 287093Lands 303642 280075 262049 234996

to an original (unaltered) image In this case the PSNRvalue would be computed in comparison with other restoredimages from the literature indicating the similarity to themand the obtained values would not be a proof of a successfulrestoration There are no original images for Lincoln andThree Girls (highly referenced in the domain) therefore

a conclusive PSNR value could be determined and only avisual analysis would be possible

However the visual restoration is satisfactory as it canbe seen from Figures 10(c) and 10(e) and is processed usingour proposedmethod for edge preserving applied toOliveirarsquosand Hadhoudrsquos methods respectively In comparison withthe original Oliveira method where the obtained edge wasblurred (as shown in Figure 10(b)) our approach offers bettercontour preservation (Figure 10(c)) Also due to the kernelused in Hadhoudrsquos method the edge is altered (Figure 10(d))However applying the proposedmethod in combinationwithHadhoudrsquos leads to good visual results (Figure 10(e)) We willconclude that using our new procedure in combination withOliveirarsquos and Hadhoudrsquos methods will offer advantages inthe case of natural defects images such as Lincoln and ThreeGirls

It was found that the algorithm proposed by Bertalmioet al successfully restores images when the method isapplied to reduced surface masks or with narrow widthbecause the contour lines crossing the area can be properlyconnected The major disadvantage of the algorithm is thatfor large masks due to diffusion a blurring effect occursand therefore the algorithm fails to restore textural images

8 The Scientific World Journal

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

35404550

849 1348 1806 2636Mask size

PSN

R

30

70656055

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 8 PSNR results for (a) helicopter and (b) lands test image

(a) (b)

(c) (d)

Figure 9 Test images with damage of (a) 983 pixels (c) 2120 pixels and the corresponding results (b) and (d) obtained with Criminisirsquosalgorithm

The method however can lead to good results using smallamount of information around the mask unlike the textureinpainting algorithms which requires a more significantamount of information in order to perform the restoration

Unlike the algorithm proposed by Bertalmio et al themethod presented by Oliveira et al is less complex Howeverthis advantage fails to compensate the fact that the contourlines can be preserved only by defining the diffusion barriersand the algorithm can be successfully applied to images withnatural damageTherefore the algorithm is suitable formaskshaving narrow width otherwise a high blurring effect can benoticed

In the case of Hadhoud et al method processing timeimprovements could be noticed as a consequence of thefact that more known neighbors of the restoring pixel areused Hence the required number of iterations considerablydecreases Similarly to the Oliveira et al method the algo-rithm is suitable for restoring images that do not have highcontrast

The texture synthesis algorithm proposed by Efros andLeung led to impressive results Although in contrast toother methods the numerical values may be less satisfactorybecause the stochastic textures would be impossible torestore The restored pixels have been assigned a close value

The Scientific World Journal 9

(a) (b)

(c) (d)

(e)

Figure 10 (a) Original image (b) result for Oliveirarsquos method (c) result for our adaptation of Oliveirarsquos method (d) result for Hadhoudrsquosmethod (e) result for our adaptation of Hadhoudrsquos method

to the original one as inpainting is done by copying pixelsfromapredetermined area andnot by propagation of externalinformation The method performs well also for structuralimages but the main disadvantage consists of the extremelylong processing time caused by the pixel by pixel restoration

The Criminisi method leads to good results both forstructural and textural images since it takes into consid-eration structural information Unlike the Efros and Leungalgorithm restoration is performed block by block reducingthe processing time As a consequence a disadvantage may

occur when choosing too large blocks for replication asinappropriate information can be copied inside the occludedarea The quality of the results heavily depends on thisparameter but also on the provided context by means of asecond parameter which specifies the source bandwidth

5 Conclusions

The paper presents a comparative study regarding inpaintingtechniques in order to evaluate different types of image

10 The Scientific World Journal

restoration methods and to emphasize the advantages anddisadvantages for each of the approached algorithms

Due to the fact that a certain number of inpaintingmethods have been proposed during the last years it is stilldifficult to designate the appropriate one The algorithmschosen for our evaluation are representative for the categoriesthey belong to having as reference the first one developedby Bertalmio Other methods were also analyzed as theone proposed by Oliveira and its adapted version proposedby Hadhoud et al suitable for images without texturesRegarding these two methods an alternative to the diffusionbarriers was proposed by us The restoration of texturedimages had also been taken into account in our evaluationby using the method developed by Efros and Leung and thealgorithm proposed by Criminisi

It was also important to determine the algorithm param-eters that lead to the best PSNR results and selectingrepresentative test images to provide relevant informationThe images were restored gradually varying the width ofthe occluded area in order to analyze the influence of thisparameter The tests have shown that inpainting algorithmsinvolving diffusion operations perform well for structuralfeatures images but cannot successfully rebuild textures

Image restoration using the RGB color system for thealgorithm developed by Bertalmio led to successful resultsfor structural images The adaptation proposed for Oliveirarsquosand Hadhoudrsquos algorithms has been proven to be a successfulalternative for edge preserving with remarkable resultsHowever textural inpainting techniques are themost success-ful Even if requiring a longer processing time they performwell on both image types

Further developments of this work may consist ofimplementing hybrid methods that combine features of theapproached algorithms and comparing their results with theones belonging to the already analyzed methods Hybridmethods would require reconstruction processes for thecontour lines and restoration processes over the obtainedregions by means of textural inpainting techniques

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] P Patel A Prajapati and SMishra ldquoReviewof different inpaint-ing algorithmsrdquo International Journal of Computer Applicationsvol 59 no 18 pp 30ndash34 2012

[2] M E Taschler ldquoA comparative analysis of image inpaintingrdquoTech Rep University of York York UK 2006

[3] C Guillemot and O Le Meur ldquoImage inpainting overview andrecent advancesrdquo IEEE Signal Processing Magazine vol 31 pp127ndash144 2014

[4] M Bertalmio G Sapiro V Caselles and C Ballester ldquoImageinpaintingrdquo in Proceedings of the 27th annual conference onComputer graphics and interactive techniques (SIGGRAPH rsquo00)pp 417ndash424 July 2000

[5] A BugeauM Bertalmio V Caselles and G Sapiro ldquoA compre-hensive framework for image inpaintingrdquo IEEE Transactions onImage Processing vol 19 no 10 pp 2634ndash2645 2010

[6] T F Chan and J Shen ldquoNontexture inpainting by curvature-driven diffusionsrdquo Journal of Visual Communication and ImageRepresentation vol 12 no 4 pp 436ndash449 2001

[7] D Tschumperle and R Deriche ldquoVector-valued image regular-ization with PDEs a common framework for different appli-cationsrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 27 no 4 pp 506ndash517 2005

[8] J Sun L Yuan J Jia and H Y Shum ldquoImage completion withstructure propagationrdquo ACM Transactions on Graphics vol 24pp 861ndash868 2005

[9] M M Oliviera B Bowen R McKenna and Y S Chang ldquoFastdigital image inpaintingrdquo in Proceedings of the InternationalConference on Visualization Imaging and Image Processing(VIIP rsquo01) pp 261ndash266 2001

[10] A Telea ldquoAn image inpainting technique based on the fastmarching methodrdquo Journal of Graphics Tools vol 9 pp 23ndash342004

[11] B Yan Y Gao K Sun and B Yang ldquoEfficient seam carvingfor object removalrdquo in Proceedings of the 20th IEEE Interna-tional Conference on Image Processing (ICIP rsquo13) pp 1331ndash1335September 2013

[12] A A Efros and T K Leung ldquoTexture synthesis by non-parametric samplingrdquo in Proceedings of the 7th IEEE Interna-tional Conference onComputer Vision (ICCVrsquo99) pp 1033ndash1038Corfu Greece September 1999

[13] A A Efros and W T Freeman ldquoImage quilting for texturesynthesis and transferrdquo in Proceedings of the 28th AnnualConference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo01) pp 341ndash346 Los Angeles Calif USA August2001

[14] D J Heeger and J R Bergen ldquoPyramid-based texture anal-ysissynthesisrdquo in Proceedings of the 22nd Annual ACM Con-ference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo95) vol 29 pp 229ndash238 Los Angeles Calif USAAugust 1995

[15] J S de Bonet ldquoMultiresolution sampling procedure for analysisand synthesis of texture imagesrdquo in Proceedings of the 24thAnnual Conference on Computer Graphics and Interactive Tech-niques (SIGGRAPH rsquo97) pp 361ndash368 Los Angeles Calif USAAugust 1997

[16] H Igehy and L Pereira ldquoImage replacement through texturesynthesisrdquo in Proceedings of the International Conference onImage Processing vol 3 pp 186ndash189 Santa Barbara Calif USAOctober 1997

[17] A Criminisi P Perez and K Toyama ldquoRegion filling andobject removal by exemplar-based image inpaintingrdquo IEEETransactions on Image Processing vol 13 no 9 pp 1200ndash12122004

[18] I Drori D Cohen-Or and H Yeshurun ldquoFragmentmdashbasedimage completionrdquo ACM Transactions on Graphics vol 22 pp303ndash312 2003

[19] C Guillemot M Turkan O L Meur and M Ebdelli ldquoImageinpainting using LLE-LDNR and linear subspace mappingsrdquo inProceedings of the IEEE International Conference on AcousticsSpeech and Signal Processing (ICASSP rsquo13) pp 1558ndash1562 May2013

[20] J Hays and A Efros ldquoScene completion using millions ofphotographsrdquo ACM Transactions on Graphics (SIGGRAPH2007) vol 26 no 3 2007

The Scientific World Journal 11

[21] O LeMeur and C Guillemot ldquoSuper-resolution-based inpaint-ingrdquo in Proceedings of European Conference on Computer Vision(ECCV rsquo12) pp 554ndash567 2012

[22] Z Xu and J Sun ldquoImage inpainting by patch propagation usingpatch sparsityrdquo IEEE Transactions on Image Processing vol 19no 5 pp 1153ndash1165 2010

[23] J Aujol S Ladjal and S Masnou ldquoExemplar-based inpaintingfrom a variational point of viewrdquo SIAM Journal onMathematicalAnalysis vol 42 no 3 pp 1246ndash1285 2010

[24] M Bertalmio L Vese G Sapiro and S Osher ldquoSimultaneousstructure and texture image inpaintingrdquo IEEE Transactions onImage Processing vol 12 no 8 pp 882ndash889 2003

[25] L Atzori and F G B de Natale ldquoError concealment in videotransmission over packet networks by a sketch-based approachrdquoSignal Processing ImageCommunication vol 15 no 1 pp 57ndash761999

[26] A Rares M J T Reinders and J Biemond ldquoEdge-based imagerestorationrdquo IEEE Transactions on Image Processing vol 14 no10 pp 1454ndash1468 2005

[27] M M Hadhoud K A Moustafa and S Z Shenoda ldquoDigitalimages inpainting using modified convolution based methodrdquoinOptical Pattern Recognition XX vol 7340 of Proceedings of theSPIE Orlando Fla USA April 2009

[28] P Perona and J Malik ldquoScale-space and edge detection usinganisotropic diffusionrdquo IEEE Transactions on Pattern Analysisand Machine Intelligence vol 12 no 7 pp 629ndash639 1990

[29] M Daisy D Tschumperle and O Lezoray ldquoA fast spatialpatch blending algorithm for artefact reduction in pattern-based image inpaintingrdquo in SIGGRAPH Asia 2013 TechnicalBriefs (SA rsquo13) pp 1ndash4 article 8 ACM New York NY USA2013

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Submit your manuscripts athttpwwwhindawicom

VLSI Design

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Electrical and Computer Engineering

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Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

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Chemical EngineeringInternational Journal of Antennas and

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Navigation and Observation

International Journal of

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DistributedSensor Networks

International Journal of

The Scientific World Journal 3

image representing an improved version of the previous oneConsider

119868119899+1

(119894 119895) = 119868119899

(119894 119895) + Δ119905119868119899

119905(119894 119895) forall (119894 119895) isin Ω (1)

where 119868119899+1(119894 119895) is the intensity of the pixel having thecoordinates (119894 119895) at moment 119899 + 1 Δ119905 is the improvement orchange rate and 119868119899

119905(119894 119895) corresponds to an image update at

time 119899This update includes the information to be propagatedand the direction of propagation as follows

119868119899

119905(119894 119895) = (

997888997888rarr120575119871119899

(119894 119895) sdot (119894 119895 119899)

10038161003816100381610038161003816 (119894 119895 119899)

10038161003816100381610038161003816

)1003816100381610038161003816nabla119868119899

(119894 119895)1003816100381610038161003816 (2)

where997888997888rarr120575119871119899 is a vector indicating the intensity change in

the image obtained after applying the Laplace operator Theisophote line direction is expressed as follows

(119894 119895 119899)

10038161003816100381610038161003816 (119894 119895 119899)

10038161003816100381610038161003816

=

(minus119868119899

119910(119894 119895) 119868

119899

119909(119894 119895))

radic(119868119899119909(119894 119895))

2

+ (119868119899119910(119894 119895))

2

+ 120576

(3)

where 120576 is a small value intended to avoid potential divisionby 0 and 119868119899

119909 119868119899119910are intensities determined by the difference

between the intensities of the next pixel and the previousone The slope limited norm of the gradient has the aim ofimproving the stability1003816100381610038161003816nabla119868119899

(119894 119895)1003816100381610038161003816

=

radic(119868119899

119909119887119898)2

+ (119868119899

119909119891119872)

2

+ (119868119899

119910119887119898)

2

+ (119868119899

119910119891119872)

2

120573119899

gt 0

radic(119868119899

119909119887119872)2

+ (119868119899

119909119891119898)

2

+ (119868119899

119910119887119872)

2

+ (119868119899

119910119891119898)

2

120573119899

lt 0

120573119899

(119894 119895) =997888997888rarr120575119871119899

(119894 119895) sdot (119894 119895 119899)

10038161003816100381610038161003816 (119894 119895 119899)

10038161003816100381610038161003816

(4)

Indices 119887 and 119891 specify the difference between the intensitiesof the current pixel and the one in the reverse direction orforward on OX and OY coordinate axes Indices 119898 and 119872express the fact that the minimum or the maximum valuebetween the obtained result and 0 will be chosen

The method proposed by Bertalmio et al interleaves anumber of 119860 inpainting steps with 119861 anisotropic diffusionsteps whereA B and T (total number of iterations) are inputparameters We have used an anisotropic diffusion proposedby Perona and Malik [28] presenting a function limiting thediffusion process to homogeneous regions

119868119905+1

119894119895= 119868119905

119894119895+ 120582[119888119873sdot nabla119873119868 + 119888119878sdot nabla119878119868 + 119888119864sdot nabla119864119868 + 119888119882sdot nabla119882119868]119905

119894119895

(5)

where 119868119905+1119894119895

is the intensity of the pixel having the (119894 119895)coordinates at 119905 + 1 moment 120582 is a constant value whichshould be in the range [0 025] for algorithm stability

a a

aa

b

b b

b

0 0

c c c

c c c

c c

Figure 1 The convolution kernels proposed by Oliviera et al [9]

and nabla119873119868 nabla119878119868 nabla119864119868 nabla119882119868 represent the difference between the

intensities of the pixel in the direction indicated by the index(north south east or west) and the current pixel

nabla119873119868119894119895equiv 119868119894119895minus1

minus 119868119894119895

nabla119878119868119894119895equiv 119868119894119895+1

minus 119868119894119895

nabla119864119868119894119895equiv 119868119894+1119895

minus 119868119894119895

nabla119882119868119894119895equiv 119868119894minus1119895

minus 119868119894119895

(6)

with 119888119873 119888119878 119888119864 119888119882called conduction coefficients determined

based on the gradient There are several methods to computethese values including the following two proposed by theauthors

119888 (nabla119868) = 119890minus(nabla119868119870)

2

(7)

119888 (nabla119868) =1

1 + (nabla119868119870)2 (8)

The coefficients are determined using one of (7) or (8) wherethe gradient nabla119868 corresponding to the direction described bythe index and119870 controls the sensitivity of the edge detectionprocess Both the inpainting stage itself and the anisotropicdiffusion method will be applied to the RGB components ofthe pixel

22 Oliveirarsquos Algorithm Based on the previous methodOliviera et al [9] have proposed an inpainting algorithm thatrelies exclusively on diffusionThe processing steps consist ofdeleting color information inside the mask followed by edgedetection for the occluded area Starting from the pixels onthe edge a convolution operation is then applied using aneighborhood centered on each contour pixel and one of thekernels proposed (Figure 1) The values of a b and 119888 for bothkernels are 0073235 0176765 and 0125 respectively [9]

23 Hadhoud Moustafa and Shenodarsquos Algorithm Hadhoudet al [27] have proposed an improvement of Oliveirarsquosmethod regarding both the final image and the requiredprocessing time Some steps have been kept from the originalmethod of [9] involving the selection of the mask followedby the removal of the existing color information in the maskUnlike Oliveirarsquos algorithm the method uses a differentlydefined convolution kernel The idea was to use as much

4 The Scientific World Journal

a a

a

a

b

b b

b 00

c c c

c c

c c

c

Figure 2The convolution kernels proposed by Hadhoud et al [27]

as possible information from outside of the region in viewof the restoration process (Figure 2) By using more knownneighbors the restoration can be achieved even within asingle iteration

24 Efros and Leungrsquos Algorithm The algorithm steps includedefining a mask and specifying a source area followed bythe edge detection for the occluded area [12] All pixels onthe edge will be sorted in descending order by the numberof known neighbors A template will be defined centeredfor each pixel chosen for restoration This window has aparameterized size and it will be used in searching for similarblocks in the source area The similarity measure is given bythe sum of squared differences (SSD) To preserve the localcharacter of the texture a Gaussian kernel is used which aimsto control the influence of pixels located too far from theoccluded area Consider

119889 = 119889SSD lowast 119866 (9)

Depending on the SSD value a collection of candidate blockswill be obtained Consider

120596best = arg min120596

119889 (120596 (119901) 120596) sub 119868smp

119889 (120596 (119901) 120596) lt (1 + 120576) 119889 (120596 (119901) 120596best) 120576 = 01

(10)

where the processed pixel is119901 119868smp represents the source areaand 119889(120596(119901) 120596) describes the distance from a 120596 sized windowcentered on pixel 119901 to a block of the same size 120596 found in thesource One of the candidate blocks will be chosen randomlyand the color information of its center pixel will be assignedto the pixel on the edge (the center of the window template)

25 Criminisirsquos Algorithm This algorithm aims to achievetexture synthesis taking into consideration structural infor-mation such as the isophote lines that cross the edge of theoccluded area [17] It consists of three major steps and startswith the pixels on the edge 120597Ω of themaskΩ For all windowscentered on edge pixels a priority 119875(119901) is computed where 119901represents the processed pixel at a certain moment

119875 (119901) = 119862 (119901) lowast 119863 (119901) (11)

where 119862(119901) represents a confidence term associated with ablock 120595

119901(the higher the number of known pixels in the

window the higher the confidence) 119863(119901) is a term that

processes the structural information contained in thewindow120595119901and raises the priority of a block comprising an isophote

line These two terms are defined as follows

119862 (119901) =

sum119902isin120595119901cap(119868minusΩ)

119862 (119902)

10038161003816100381610038161003816120595119901

10038161003816100381610038161003816

119863 (119901) =

10038161003816100381610038161003816nabla119868perp

119901119899119901

10038161003816100381610038161003816

120572

(12)

with |120595119901| the surface of the window 120595

119901centered in pixel 119901

belonging to 120597Ω where 120572 is a normalization factor with value255 119899

119901is the normal to the contour 120597Ω at point 119901 and nabla119868perp

119901is

the normal to the gradient namely the isophote lineFor the priorities 119875(119901) an initialization step is required

All pixels belonging to the mask have the confidence term119862(119901) = 0 and the ones belonging to the source band havethe confidence 119862(119901) = 1

The second processing step represented the inpaintingitself The pixel having the highest priority is the first to beprocessed its associated source block from the source area isthe one that leads to a minimal SSD distance

120595119902= arg min120595119902isinΦ

119889 (120595119901 120595119902) (13)

where 119889(120595119901 120595119902) represents the SSD value (between all known

pixels of the window 120595119901and the ones on the corresponding

positions in a block 120595119902belonging to the source band)

Knowing the source window 120595119902 all pixels of 120595

119901that also

belong to the mask will be filled with information providedby the corresponding pixels in 120595

119902 The last step consists of

updating the confidence values associated with pixels in therestored window

119862 (119901) = 119862 (119901) forall119901 isin 120595119901cap Ω (14)

3 A Proposed Adaptation of Oliveirarsquos andHadhoudrsquos Algorithms

Concerning the algorithm developed by Oliveira and itsadaptation proposed byHadhoud et al [27] conserving edgesis one of themajor problemsTherefore Oliveira et al definedsome diffusion barriers over the contour in order to stop theisotropic diffusion process otherwise some visible blurringeffects may occur However in the case of Hadhoud et alredefining the kernel and the direction of propagation leads toevenmore highlighted blurring effects and the loss of contourlines

As an alternative to the 2-pixel width barriers definedaccording to Oliveirarsquos idea we are proposing an edgeconserving procedure by defining an additional mask thatcomprises the contour The mask will be processed usingan anisotropic diffusion operation described in Bertalmiorsquosalgorithm The mask pixels are excluded from the initialmask and will no longer be modified using one of thekernels of isotropic smoothing operation As a result the userintervention is simplified and the results are satisfactory

Oliveirarsquos and Hadhoudrsquos methods are suited for imageswith natural defects such as Lincoln Unfortunately the

The Scientific World Journal 5

(a) (b)

(c) (d)

(e) (f)

Figure 3 Visual comparison of the proposed methods (a) simulation of a natural defect (b) the corresponding masks (c) result of theOliveira method (d) result of the proposed adaptation of Oliveira method (e) result of the Hadhoud method (f) result of the proposedadaptation of Hadhoud method

original image (without defects) does not exist thereforewe could not compute the PSNR in comparison to it Inorder to reach a conclusion regarding these methods and ourproposal for edge preserving some images were chosen anddefects were manually appliedTherefore the PSNR could becomputed by comparing the restored image with the originalone

In the image shown in Figure 3(a) we have applied adefect that could be considered close to a natural one Theblue mask will be processed using Oliveirarsquos or Hadhoudrsquosmethod as for the yellow mask an anisotropic diffusionwill be applied It can be noticed from the result in Figure 3and Table 1 that our proposal offers improvements regardingHadhoudrsquos method However it worth mentioning that theresults would be more relevant if images with natural defectswould have been tested and their originals could be used as aground truth

Table 1 PSNR values comparison for the proposed methods

Image Oliveira Hadhoud Our adaptation ofOliveira

Our adaptation ofHadhoud

Peppers 47155 42383 467605 43138Egipt 463948 438093 46038 4600067

4 An Evaluation of the Inpainting Algorithms

The five inpainting methods were implemented in the Cand run on a system with Intel i5 processor at 25 GHz Themethod proposed by Bertalmio et al was implemented onRGB color images The algorithm developed by Oliveira etal and the method proposed by Hadhoud et al were imple-mented taking into consideration the proposal describedabove regarding edge conservation In the case of Efros and

6 The Scientific World Journal

30

35

40

45

50

849 1348 1806 2636

Bertalmio et al (2000 2003)Oliviera et al (2001)Hadhoud et al (2009)

Efros and Leung (1999)Criminisi et al (2004)

Mask size

PSN

R

(a)

849 1348 1806 2636Mask size

PSN

R

30354045505560

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 4 PSNR results for (a) Lena and (b) Peepers test image

30

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

30

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

25

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 5 PSNR results for (a) Baboon and (b) StillLifeWithApples test image

Leungrsquos algorithm the source area was represented by aband around the occluded region [2] The same assumptionwas considered for the method proposed by Criminisi etal Our evaluation was carried out on representative testimages characterized by structural lines but also by texturecontent

First of all it was necessary to determine the optimalconfiguration of each method parameters in order to obtainthe best results in terms of PSNR Therefore several con-figurations for each algorithm were tested The test imagesused were Lena Peppers Baboon and StillLifeWithApplesas presented in [17] and Barbara Egipt cat fur fly helicopterand lands from [29] An artificial damage was applied and therestored image was compared to the original one as referenceOliveirarsquos method and the version proposed by Hadhoudet al were tested on the well-known inpainting test imagesLincoln and Three Girls due to their efficiency on naturaldamage images The main disadvantage was that there areno original images that could be used as reference in orderto compute the PSNR value Our artificial test damage wasdefined as a stripe successively widened in order to noticehow the algorithm behaves for ldquospot masksrdquo The data inTable 2 presents the mask (damage) size in pixels and thecorresponding initial PSNR values By gradually increasing

the mask width we had obtained the PSNR results presentedin Figures 4 5 6 7 and 8 for the ten considered test images

As it can be seen from the PSNR results among the struc-tural inpainting methods the one belonging to Bertalmioleads to the successful results among which Peppers andLena obtain the highest values Due to diffusion method thealgorithm has lower results for textural images in comparisonwith structural ones

For the last two methods there are some improvementsbut it is important to mention that in the case of texturalimages the PSNR value is not relevant as inpainting is per-formed by the replication of information from a source areaand not by actual propagation inside themask Consequentlyas the mask increases it is likely to obtain lower PSNR valuesand still have a very successful visual effect (as it can beseen from Figure 9) In the case of diffusion methods theresults are less successful leading to color spread and causingblurring effects

Considering the proposed adaptation for contour linepreserving of Oliveirarsquos and Hadhoudrsquos methods described inSection 3 an improvement has been noticed in comparisonto the basic algorithm which applied isotropic diffusion overthe entire mask Unfortunately since these two methods aresuitable for natural defects images they cannot be compared

The Scientific World Journal 7

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

55

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 6 PSNR results for (a) Barbara and (b) Egipt test image

35

40

849 1348 1806 2636Mask size

PSN

R

30

25

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

25

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 7 PSNR results for (a) cat fur and (b) fly test image

Table 2 The initial PSNR values depending on the mask size

Image Mask size (pixels)849 1348 1806 2636

Lena 3726898 349823 330214 3023914Peppers 3461765 321941 304215 278131Baboon 327125 301962 2843159 257326StillLifeWithApples 329604 307468 289399 263172Barbara 3343815 3112731 29333 266095Egipt 2994907 275565 2572072 230565Cat fur 3210376 2958541 2761433 251814Fly 30935 28547 2670904 239389Helicopter 353284 330786 3132791 287093Lands 303642 280075 262049 234996

to an original (unaltered) image In this case the PSNRvalue would be computed in comparison with other restoredimages from the literature indicating the similarity to themand the obtained values would not be a proof of a successfulrestoration There are no original images for Lincoln andThree Girls (highly referenced in the domain) therefore

a conclusive PSNR value could be determined and only avisual analysis would be possible

However the visual restoration is satisfactory as it canbe seen from Figures 10(c) and 10(e) and is processed usingour proposedmethod for edge preserving applied toOliveirarsquosand Hadhoudrsquos methods respectively In comparison withthe original Oliveira method where the obtained edge wasblurred (as shown in Figure 10(b)) our approach offers bettercontour preservation (Figure 10(c)) Also due to the kernelused in Hadhoudrsquos method the edge is altered (Figure 10(d))However applying the proposedmethod in combinationwithHadhoudrsquos leads to good visual results (Figure 10(e)) We willconclude that using our new procedure in combination withOliveirarsquos and Hadhoudrsquos methods will offer advantages inthe case of natural defects images such as Lincoln and ThreeGirls

It was found that the algorithm proposed by Bertalmioet al successfully restores images when the method isapplied to reduced surface masks or with narrow widthbecause the contour lines crossing the area can be properlyconnected The major disadvantage of the algorithm is thatfor large masks due to diffusion a blurring effect occursand therefore the algorithm fails to restore textural images

8 The Scientific World Journal

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

35404550

849 1348 1806 2636Mask size

PSN

R

30

70656055

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 8 PSNR results for (a) helicopter and (b) lands test image

(a) (b)

(c) (d)

Figure 9 Test images with damage of (a) 983 pixels (c) 2120 pixels and the corresponding results (b) and (d) obtained with Criminisirsquosalgorithm

The method however can lead to good results using smallamount of information around the mask unlike the textureinpainting algorithms which requires a more significantamount of information in order to perform the restoration

Unlike the algorithm proposed by Bertalmio et al themethod presented by Oliveira et al is less complex Howeverthis advantage fails to compensate the fact that the contourlines can be preserved only by defining the diffusion barriersand the algorithm can be successfully applied to images withnatural damageTherefore the algorithm is suitable formaskshaving narrow width otherwise a high blurring effect can benoticed

In the case of Hadhoud et al method processing timeimprovements could be noticed as a consequence of thefact that more known neighbors of the restoring pixel areused Hence the required number of iterations considerablydecreases Similarly to the Oliveira et al method the algo-rithm is suitable for restoring images that do not have highcontrast

The texture synthesis algorithm proposed by Efros andLeung led to impressive results Although in contrast toother methods the numerical values may be less satisfactorybecause the stochastic textures would be impossible torestore The restored pixels have been assigned a close value

The Scientific World Journal 9

(a) (b)

(c) (d)

(e)

Figure 10 (a) Original image (b) result for Oliveirarsquos method (c) result for our adaptation of Oliveirarsquos method (d) result for Hadhoudrsquosmethod (e) result for our adaptation of Hadhoudrsquos method

to the original one as inpainting is done by copying pixelsfromapredetermined area andnot by propagation of externalinformation The method performs well also for structuralimages but the main disadvantage consists of the extremelylong processing time caused by the pixel by pixel restoration

The Criminisi method leads to good results both forstructural and textural images since it takes into consid-eration structural information Unlike the Efros and Leungalgorithm restoration is performed block by block reducingthe processing time As a consequence a disadvantage may

occur when choosing too large blocks for replication asinappropriate information can be copied inside the occludedarea The quality of the results heavily depends on thisparameter but also on the provided context by means of asecond parameter which specifies the source bandwidth

5 Conclusions

The paper presents a comparative study regarding inpaintingtechniques in order to evaluate different types of image

10 The Scientific World Journal

restoration methods and to emphasize the advantages anddisadvantages for each of the approached algorithms

Due to the fact that a certain number of inpaintingmethods have been proposed during the last years it is stilldifficult to designate the appropriate one The algorithmschosen for our evaluation are representative for the categoriesthey belong to having as reference the first one developedby Bertalmio Other methods were also analyzed as theone proposed by Oliveira and its adapted version proposedby Hadhoud et al suitable for images without texturesRegarding these two methods an alternative to the diffusionbarriers was proposed by us The restoration of texturedimages had also been taken into account in our evaluationby using the method developed by Efros and Leung and thealgorithm proposed by Criminisi

It was also important to determine the algorithm param-eters that lead to the best PSNR results and selectingrepresentative test images to provide relevant informationThe images were restored gradually varying the width ofthe occluded area in order to analyze the influence of thisparameter The tests have shown that inpainting algorithmsinvolving diffusion operations perform well for structuralfeatures images but cannot successfully rebuild textures

Image restoration using the RGB color system for thealgorithm developed by Bertalmio led to successful resultsfor structural images The adaptation proposed for Oliveirarsquosand Hadhoudrsquos algorithms has been proven to be a successfulalternative for edge preserving with remarkable resultsHowever textural inpainting techniques are themost success-ful Even if requiring a longer processing time they performwell on both image types

Further developments of this work may consist ofimplementing hybrid methods that combine features of theapproached algorithms and comparing their results with theones belonging to the already analyzed methods Hybridmethods would require reconstruction processes for thecontour lines and restoration processes over the obtainedregions by means of textural inpainting techniques

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] P Patel A Prajapati and SMishra ldquoReviewof different inpaint-ing algorithmsrdquo International Journal of Computer Applicationsvol 59 no 18 pp 30ndash34 2012

[2] M E Taschler ldquoA comparative analysis of image inpaintingrdquoTech Rep University of York York UK 2006

[3] C Guillemot and O Le Meur ldquoImage inpainting overview andrecent advancesrdquo IEEE Signal Processing Magazine vol 31 pp127ndash144 2014

[4] M Bertalmio G Sapiro V Caselles and C Ballester ldquoImageinpaintingrdquo in Proceedings of the 27th annual conference onComputer graphics and interactive techniques (SIGGRAPH rsquo00)pp 417ndash424 July 2000

[5] A BugeauM Bertalmio V Caselles and G Sapiro ldquoA compre-hensive framework for image inpaintingrdquo IEEE Transactions onImage Processing vol 19 no 10 pp 2634ndash2645 2010

[6] T F Chan and J Shen ldquoNontexture inpainting by curvature-driven diffusionsrdquo Journal of Visual Communication and ImageRepresentation vol 12 no 4 pp 436ndash449 2001

[7] D Tschumperle and R Deriche ldquoVector-valued image regular-ization with PDEs a common framework for different appli-cationsrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 27 no 4 pp 506ndash517 2005

[8] J Sun L Yuan J Jia and H Y Shum ldquoImage completion withstructure propagationrdquo ACM Transactions on Graphics vol 24pp 861ndash868 2005

[9] M M Oliviera B Bowen R McKenna and Y S Chang ldquoFastdigital image inpaintingrdquo in Proceedings of the InternationalConference on Visualization Imaging and Image Processing(VIIP rsquo01) pp 261ndash266 2001

[10] A Telea ldquoAn image inpainting technique based on the fastmarching methodrdquo Journal of Graphics Tools vol 9 pp 23ndash342004

[11] B Yan Y Gao K Sun and B Yang ldquoEfficient seam carvingfor object removalrdquo in Proceedings of the 20th IEEE Interna-tional Conference on Image Processing (ICIP rsquo13) pp 1331ndash1335September 2013

[12] A A Efros and T K Leung ldquoTexture synthesis by non-parametric samplingrdquo in Proceedings of the 7th IEEE Interna-tional Conference onComputer Vision (ICCVrsquo99) pp 1033ndash1038Corfu Greece September 1999

[13] A A Efros and W T Freeman ldquoImage quilting for texturesynthesis and transferrdquo in Proceedings of the 28th AnnualConference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo01) pp 341ndash346 Los Angeles Calif USA August2001

[14] D J Heeger and J R Bergen ldquoPyramid-based texture anal-ysissynthesisrdquo in Proceedings of the 22nd Annual ACM Con-ference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo95) vol 29 pp 229ndash238 Los Angeles Calif USAAugust 1995

[15] J S de Bonet ldquoMultiresolution sampling procedure for analysisand synthesis of texture imagesrdquo in Proceedings of the 24thAnnual Conference on Computer Graphics and Interactive Tech-niques (SIGGRAPH rsquo97) pp 361ndash368 Los Angeles Calif USAAugust 1997

[16] H Igehy and L Pereira ldquoImage replacement through texturesynthesisrdquo in Proceedings of the International Conference onImage Processing vol 3 pp 186ndash189 Santa Barbara Calif USAOctober 1997

[17] A Criminisi P Perez and K Toyama ldquoRegion filling andobject removal by exemplar-based image inpaintingrdquo IEEETransactions on Image Processing vol 13 no 9 pp 1200ndash12122004

[18] I Drori D Cohen-Or and H Yeshurun ldquoFragmentmdashbasedimage completionrdquo ACM Transactions on Graphics vol 22 pp303ndash312 2003

[19] C Guillemot M Turkan O L Meur and M Ebdelli ldquoImageinpainting using LLE-LDNR and linear subspace mappingsrdquo inProceedings of the IEEE International Conference on AcousticsSpeech and Signal Processing (ICASSP rsquo13) pp 1558ndash1562 May2013

[20] J Hays and A Efros ldquoScene completion using millions ofphotographsrdquo ACM Transactions on Graphics (SIGGRAPH2007) vol 26 no 3 2007

The Scientific World Journal 11

[21] O LeMeur and C Guillemot ldquoSuper-resolution-based inpaint-ingrdquo in Proceedings of European Conference on Computer Vision(ECCV rsquo12) pp 554ndash567 2012

[22] Z Xu and J Sun ldquoImage inpainting by patch propagation usingpatch sparsityrdquo IEEE Transactions on Image Processing vol 19no 5 pp 1153ndash1165 2010

[23] J Aujol S Ladjal and S Masnou ldquoExemplar-based inpaintingfrom a variational point of viewrdquo SIAM Journal onMathematicalAnalysis vol 42 no 3 pp 1246ndash1285 2010

[24] M Bertalmio L Vese G Sapiro and S Osher ldquoSimultaneousstructure and texture image inpaintingrdquo IEEE Transactions onImage Processing vol 12 no 8 pp 882ndash889 2003

[25] L Atzori and F G B de Natale ldquoError concealment in videotransmission over packet networks by a sketch-based approachrdquoSignal Processing ImageCommunication vol 15 no 1 pp 57ndash761999

[26] A Rares M J T Reinders and J Biemond ldquoEdge-based imagerestorationrdquo IEEE Transactions on Image Processing vol 14 no10 pp 1454ndash1468 2005

[27] M M Hadhoud K A Moustafa and S Z Shenoda ldquoDigitalimages inpainting using modified convolution based methodrdquoinOptical Pattern Recognition XX vol 7340 of Proceedings of theSPIE Orlando Fla USA April 2009

[28] P Perona and J Malik ldquoScale-space and edge detection usinganisotropic diffusionrdquo IEEE Transactions on Pattern Analysisand Machine Intelligence vol 12 no 7 pp 629ndash639 1990

[29] M Daisy D Tschumperle and O Lezoray ldquoA fast spatialpatch blending algorithm for artefact reduction in pattern-based image inpaintingrdquo in SIGGRAPH Asia 2013 TechnicalBriefs (SA rsquo13) pp 1ndash4 article 8 ACM New York NY USA2013

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Submit your manuscripts athttpwwwhindawicom

VLSI Design

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Shock and Vibration

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Civil EngineeringAdvances in

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SensorsJournal of

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Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

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Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

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Navigation and Observation

International Journal of

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DistributedSensor Networks

International Journal of

4 The Scientific World Journal

a a

a

a

b

b b

b 00

c c c

c c

c c

c

Figure 2The convolution kernels proposed by Hadhoud et al [27]

as possible information from outside of the region in viewof the restoration process (Figure 2) By using more knownneighbors the restoration can be achieved even within asingle iteration

24 Efros and Leungrsquos Algorithm The algorithm steps includedefining a mask and specifying a source area followed bythe edge detection for the occluded area [12] All pixels onthe edge will be sorted in descending order by the numberof known neighbors A template will be defined centeredfor each pixel chosen for restoration This window has aparameterized size and it will be used in searching for similarblocks in the source area The similarity measure is given bythe sum of squared differences (SSD) To preserve the localcharacter of the texture a Gaussian kernel is used which aimsto control the influence of pixels located too far from theoccluded area Consider

119889 = 119889SSD lowast 119866 (9)

Depending on the SSD value a collection of candidate blockswill be obtained Consider

120596best = arg min120596

119889 (120596 (119901) 120596) sub 119868smp

119889 (120596 (119901) 120596) lt (1 + 120576) 119889 (120596 (119901) 120596best) 120576 = 01

(10)

where the processed pixel is119901 119868smp represents the source areaand 119889(120596(119901) 120596) describes the distance from a 120596 sized windowcentered on pixel 119901 to a block of the same size 120596 found in thesource One of the candidate blocks will be chosen randomlyand the color information of its center pixel will be assignedto the pixel on the edge (the center of the window template)

25 Criminisirsquos Algorithm This algorithm aims to achievetexture synthesis taking into consideration structural infor-mation such as the isophote lines that cross the edge of theoccluded area [17] It consists of three major steps and startswith the pixels on the edge 120597Ω of themaskΩ For all windowscentered on edge pixels a priority 119875(119901) is computed where 119901represents the processed pixel at a certain moment

119875 (119901) = 119862 (119901) lowast 119863 (119901) (11)

where 119862(119901) represents a confidence term associated with ablock 120595

119901(the higher the number of known pixels in the

window the higher the confidence) 119863(119901) is a term that

processes the structural information contained in thewindow120595119901and raises the priority of a block comprising an isophote

line These two terms are defined as follows

119862 (119901) =

sum119902isin120595119901cap(119868minusΩ)

119862 (119902)

10038161003816100381610038161003816120595119901

10038161003816100381610038161003816

119863 (119901) =

10038161003816100381610038161003816nabla119868perp

119901119899119901

10038161003816100381610038161003816

120572

(12)

with |120595119901| the surface of the window 120595

119901centered in pixel 119901

belonging to 120597Ω where 120572 is a normalization factor with value255 119899

119901is the normal to the contour 120597Ω at point 119901 and nabla119868perp

119901is

the normal to the gradient namely the isophote lineFor the priorities 119875(119901) an initialization step is required

All pixels belonging to the mask have the confidence term119862(119901) = 0 and the ones belonging to the source band havethe confidence 119862(119901) = 1

The second processing step represented the inpaintingitself The pixel having the highest priority is the first to beprocessed its associated source block from the source area isthe one that leads to a minimal SSD distance

120595119902= arg min120595119902isinΦ

119889 (120595119901 120595119902) (13)

where 119889(120595119901 120595119902) represents the SSD value (between all known

pixels of the window 120595119901and the ones on the corresponding

positions in a block 120595119902belonging to the source band)

Knowing the source window 120595119902 all pixels of 120595

119901that also

belong to the mask will be filled with information providedby the corresponding pixels in 120595

119902 The last step consists of

updating the confidence values associated with pixels in therestored window

119862 (119901) = 119862 (119901) forall119901 isin 120595119901cap Ω (14)

3 A Proposed Adaptation of Oliveirarsquos andHadhoudrsquos Algorithms

Concerning the algorithm developed by Oliveira and itsadaptation proposed byHadhoud et al [27] conserving edgesis one of themajor problemsTherefore Oliveira et al definedsome diffusion barriers over the contour in order to stop theisotropic diffusion process otherwise some visible blurringeffects may occur However in the case of Hadhoud et alredefining the kernel and the direction of propagation leads toevenmore highlighted blurring effects and the loss of contourlines

As an alternative to the 2-pixel width barriers definedaccording to Oliveirarsquos idea we are proposing an edgeconserving procedure by defining an additional mask thatcomprises the contour The mask will be processed usingan anisotropic diffusion operation described in Bertalmiorsquosalgorithm The mask pixels are excluded from the initialmask and will no longer be modified using one of thekernels of isotropic smoothing operation As a result the userintervention is simplified and the results are satisfactory

Oliveirarsquos and Hadhoudrsquos methods are suited for imageswith natural defects such as Lincoln Unfortunately the

The Scientific World Journal 5

(a) (b)

(c) (d)

(e) (f)

Figure 3 Visual comparison of the proposed methods (a) simulation of a natural defect (b) the corresponding masks (c) result of theOliveira method (d) result of the proposed adaptation of Oliveira method (e) result of the Hadhoud method (f) result of the proposedadaptation of Hadhoud method

original image (without defects) does not exist thereforewe could not compute the PSNR in comparison to it Inorder to reach a conclusion regarding these methods and ourproposal for edge preserving some images were chosen anddefects were manually appliedTherefore the PSNR could becomputed by comparing the restored image with the originalone

In the image shown in Figure 3(a) we have applied adefect that could be considered close to a natural one Theblue mask will be processed using Oliveirarsquos or Hadhoudrsquosmethod as for the yellow mask an anisotropic diffusionwill be applied It can be noticed from the result in Figure 3and Table 1 that our proposal offers improvements regardingHadhoudrsquos method However it worth mentioning that theresults would be more relevant if images with natural defectswould have been tested and their originals could be used as aground truth

Table 1 PSNR values comparison for the proposed methods

Image Oliveira Hadhoud Our adaptation ofOliveira

Our adaptation ofHadhoud

Peppers 47155 42383 467605 43138Egipt 463948 438093 46038 4600067

4 An Evaluation of the Inpainting Algorithms

The five inpainting methods were implemented in the Cand run on a system with Intel i5 processor at 25 GHz Themethod proposed by Bertalmio et al was implemented onRGB color images The algorithm developed by Oliveira etal and the method proposed by Hadhoud et al were imple-mented taking into consideration the proposal describedabove regarding edge conservation In the case of Efros and

6 The Scientific World Journal

30

35

40

45

50

849 1348 1806 2636

Bertalmio et al (2000 2003)Oliviera et al (2001)Hadhoud et al (2009)

Efros and Leung (1999)Criminisi et al (2004)

Mask size

PSN

R

(a)

849 1348 1806 2636Mask size

PSN

R

30354045505560

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 4 PSNR results for (a) Lena and (b) Peepers test image

30

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

30

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

25

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 5 PSNR results for (a) Baboon and (b) StillLifeWithApples test image

Leungrsquos algorithm the source area was represented by aband around the occluded region [2] The same assumptionwas considered for the method proposed by Criminisi etal Our evaluation was carried out on representative testimages characterized by structural lines but also by texturecontent

First of all it was necessary to determine the optimalconfiguration of each method parameters in order to obtainthe best results in terms of PSNR Therefore several con-figurations for each algorithm were tested The test imagesused were Lena Peppers Baboon and StillLifeWithApplesas presented in [17] and Barbara Egipt cat fur fly helicopterand lands from [29] An artificial damage was applied and therestored image was compared to the original one as referenceOliveirarsquos method and the version proposed by Hadhoudet al were tested on the well-known inpainting test imagesLincoln and Three Girls due to their efficiency on naturaldamage images The main disadvantage was that there areno original images that could be used as reference in orderto compute the PSNR value Our artificial test damage wasdefined as a stripe successively widened in order to noticehow the algorithm behaves for ldquospot masksrdquo The data inTable 2 presents the mask (damage) size in pixels and thecorresponding initial PSNR values By gradually increasing

the mask width we had obtained the PSNR results presentedin Figures 4 5 6 7 and 8 for the ten considered test images

As it can be seen from the PSNR results among the struc-tural inpainting methods the one belonging to Bertalmioleads to the successful results among which Peppers andLena obtain the highest values Due to diffusion method thealgorithm has lower results for textural images in comparisonwith structural ones

For the last two methods there are some improvementsbut it is important to mention that in the case of texturalimages the PSNR value is not relevant as inpainting is per-formed by the replication of information from a source areaand not by actual propagation inside themask Consequentlyas the mask increases it is likely to obtain lower PSNR valuesand still have a very successful visual effect (as it can beseen from Figure 9) In the case of diffusion methods theresults are less successful leading to color spread and causingblurring effects

Considering the proposed adaptation for contour linepreserving of Oliveirarsquos and Hadhoudrsquos methods described inSection 3 an improvement has been noticed in comparisonto the basic algorithm which applied isotropic diffusion overthe entire mask Unfortunately since these two methods aresuitable for natural defects images they cannot be compared

The Scientific World Journal 7

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

55

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 6 PSNR results for (a) Barbara and (b) Egipt test image

35

40

849 1348 1806 2636Mask size

PSN

R

30

25

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

25

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 7 PSNR results for (a) cat fur and (b) fly test image

Table 2 The initial PSNR values depending on the mask size

Image Mask size (pixels)849 1348 1806 2636

Lena 3726898 349823 330214 3023914Peppers 3461765 321941 304215 278131Baboon 327125 301962 2843159 257326StillLifeWithApples 329604 307468 289399 263172Barbara 3343815 3112731 29333 266095Egipt 2994907 275565 2572072 230565Cat fur 3210376 2958541 2761433 251814Fly 30935 28547 2670904 239389Helicopter 353284 330786 3132791 287093Lands 303642 280075 262049 234996

to an original (unaltered) image In this case the PSNRvalue would be computed in comparison with other restoredimages from the literature indicating the similarity to themand the obtained values would not be a proof of a successfulrestoration There are no original images for Lincoln andThree Girls (highly referenced in the domain) therefore

a conclusive PSNR value could be determined and only avisual analysis would be possible

However the visual restoration is satisfactory as it canbe seen from Figures 10(c) and 10(e) and is processed usingour proposedmethod for edge preserving applied toOliveirarsquosand Hadhoudrsquos methods respectively In comparison withthe original Oliveira method where the obtained edge wasblurred (as shown in Figure 10(b)) our approach offers bettercontour preservation (Figure 10(c)) Also due to the kernelused in Hadhoudrsquos method the edge is altered (Figure 10(d))However applying the proposedmethod in combinationwithHadhoudrsquos leads to good visual results (Figure 10(e)) We willconclude that using our new procedure in combination withOliveirarsquos and Hadhoudrsquos methods will offer advantages inthe case of natural defects images such as Lincoln and ThreeGirls

It was found that the algorithm proposed by Bertalmioet al successfully restores images when the method isapplied to reduced surface masks or with narrow widthbecause the contour lines crossing the area can be properlyconnected The major disadvantage of the algorithm is thatfor large masks due to diffusion a blurring effect occursand therefore the algorithm fails to restore textural images

8 The Scientific World Journal

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

35404550

849 1348 1806 2636Mask size

PSN

R

30

70656055

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 8 PSNR results for (a) helicopter and (b) lands test image

(a) (b)

(c) (d)

Figure 9 Test images with damage of (a) 983 pixels (c) 2120 pixels and the corresponding results (b) and (d) obtained with Criminisirsquosalgorithm

The method however can lead to good results using smallamount of information around the mask unlike the textureinpainting algorithms which requires a more significantamount of information in order to perform the restoration

Unlike the algorithm proposed by Bertalmio et al themethod presented by Oliveira et al is less complex Howeverthis advantage fails to compensate the fact that the contourlines can be preserved only by defining the diffusion barriersand the algorithm can be successfully applied to images withnatural damageTherefore the algorithm is suitable formaskshaving narrow width otherwise a high blurring effect can benoticed

In the case of Hadhoud et al method processing timeimprovements could be noticed as a consequence of thefact that more known neighbors of the restoring pixel areused Hence the required number of iterations considerablydecreases Similarly to the Oliveira et al method the algo-rithm is suitable for restoring images that do not have highcontrast

The texture synthesis algorithm proposed by Efros andLeung led to impressive results Although in contrast toother methods the numerical values may be less satisfactorybecause the stochastic textures would be impossible torestore The restored pixels have been assigned a close value

The Scientific World Journal 9

(a) (b)

(c) (d)

(e)

Figure 10 (a) Original image (b) result for Oliveirarsquos method (c) result for our adaptation of Oliveirarsquos method (d) result for Hadhoudrsquosmethod (e) result for our adaptation of Hadhoudrsquos method

to the original one as inpainting is done by copying pixelsfromapredetermined area andnot by propagation of externalinformation The method performs well also for structuralimages but the main disadvantage consists of the extremelylong processing time caused by the pixel by pixel restoration

The Criminisi method leads to good results both forstructural and textural images since it takes into consid-eration structural information Unlike the Efros and Leungalgorithm restoration is performed block by block reducingthe processing time As a consequence a disadvantage may

occur when choosing too large blocks for replication asinappropriate information can be copied inside the occludedarea The quality of the results heavily depends on thisparameter but also on the provided context by means of asecond parameter which specifies the source bandwidth

5 Conclusions

The paper presents a comparative study regarding inpaintingtechniques in order to evaluate different types of image

10 The Scientific World Journal

restoration methods and to emphasize the advantages anddisadvantages for each of the approached algorithms

Due to the fact that a certain number of inpaintingmethods have been proposed during the last years it is stilldifficult to designate the appropriate one The algorithmschosen for our evaluation are representative for the categoriesthey belong to having as reference the first one developedby Bertalmio Other methods were also analyzed as theone proposed by Oliveira and its adapted version proposedby Hadhoud et al suitable for images without texturesRegarding these two methods an alternative to the diffusionbarriers was proposed by us The restoration of texturedimages had also been taken into account in our evaluationby using the method developed by Efros and Leung and thealgorithm proposed by Criminisi

It was also important to determine the algorithm param-eters that lead to the best PSNR results and selectingrepresentative test images to provide relevant informationThe images were restored gradually varying the width ofthe occluded area in order to analyze the influence of thisparameter The tests have shown that inpainting algorithmsinvolving diffusion operations perform well for structuralfeatures images but cannot successfully rebuild textures

Image restoration using the RGB color system for thealgorithm developed by Bertalmio led to successful resultsfor structural images The adaptation proposed for Oliveirarsquosand Hadhoudrsquos algorithms has been proven to be a successfulalternative for edge preserving with remarkable resultsHowever textural inpainting techniques are themost success-ful Even if requiring a longer processing time they performwell on both image types

Further developments of this work may consist ofimplementing hybrid methods that combine features of theapproached algorithms and comparing their results with theones belonging to the already analyzed methods Hybridmethods would require reconstruction processes for thecontour lines and restoration processes over the obtainedregions by means of textural inpainting techniques

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] P Patel A Prajapati and SMishra ldquoReviewof different inpaint-ing algorithmsrdquo International Journal of Computer Applicationsvol 59 no 18 pp 30ndash34 2012

[2] M E Taschler ldquoA comparative analysis of image inpaintingrdquoTech Rep University of York York UK 2006

[3] C Guillemot and O Le Meur ldquoImage inpainting overview andrecent advancesrdquo IEEE Signal Processing Magazine vol 31 pp127ndash144 2014

[4] M Bertalmio G Sapiro V Caselles and C Ballester ldquoImageinpaintingrdquo in Proceedings of the 27th annual conference onComputer graphics and interactive techniques (SIGGRAPH rsquo00)pp 417ndash424 July 2000

[5] A BugeauM Bertalmio V Caselles and G Sapiro ldquoA compre-hensive framework for image inpaintingrdquo IEEE Transactions onImage Processing vol 19 no 10 pp 2634ndash2645 2010

[6] T F Chan and J Shen ldquoNontexture inpainting by curvature-driven diffusionsrdquo Journal of Visual Communication and ImageRepresentation vol 12 no 4 pp 436ndash449 2001

[7] D Tschumperle and R Deriche ldquoVector-valued image regular-ization with PDEs a common framework for different appli-cationsrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 27 no 4 pp 506ndash517 2005

[8] J Sun L Yuan J Jia and H Y Shum ldquoImage completion withstructure propagationrdquo ACM Transactions on Graphics vol 24pp 861ndash868 2005

[9] M M Oliviera B Bowen R McKenna and Y S Chang ldquoFastdigital image inpaintingrdquo in Proceedings of the InternationalConference on Visualization Imaging and Image Processing(VIIP rsquo01) pp 261ndash266 2001

[10] A Telea ldquoAn image inpainting technique based on the fastmarching methodrdquo Journal of Graphics Tools vol 9 pp 23ndash342004

[11] B Yan Y Gao K Sun and B Yang ldquoEfficient seam carvingfor object removalrdquo in Proceedings of the 20th IEEE Interna-tional Conference on Image Processing (ICIP rsquo13) pp 1331ndash1335September 2013

[12] A A Efros and T K Leung ldquoTexture synthesis by non-parametric samplingrdquo in Proceedings of the 7th IEEE Interna-tional Conference onComputer Vision (ICCVrsquo99) pp 1033ndash1038Corfu Greece September 1999

[13] A A Efros and W T Freeman ldquoImage quilting for texturesynthesis and transferrdquo in Proceedings of the 28th AnnualConference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo01) pp 341ndash346 Los Angeles Calif USA August2001

[14] D J Heeger and J R Bergen ldquoPyramid-based texture anal-ysissynthesisrdquo in Proceedings of the 22nd Annual ACM Con-ference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo95) vol 29 pp 229ndash238 Los Angeles Calif USAAugust 1995

[15] J S de Bonet ldquoMultiresolution sampling procedure for analysisand synthesis of texture imagesrdquo in Proceedings of the 24thAnnual Conference on Computer Graphics and Interactive Tech-niques (SIGGRAPH rsquo97) pp 361ndash368 Los Angeles Calif USAAugust 1997

[16] H Igehy and L Pereira ldquoImage replacement through texturesynthesisrdquo in Proceedings of the International Conference onImage Processing vol 3 pp 186ndash189 Santa Barbara Calif USAOctober 1997

[17] A Criminisi P Perez and K Toyama ldquoRegion filling andobject removal by exemplar-based image inpaintingrdquo IEEETransactions on Image Processing vol 13 no 9 pp 1200ndash12122004

[18] I Drori D Cohen-Or and H Yeshurun ldquoFragmentmdashbasedimage completionrdquo ACM Transactions on Graphics vol 22 pp303ndash312 2003

[19] C Guillemot M Turkan O L Meur and M Ebdelli ldquoImageinpainting using LLE-LDNR and linear subspace mappingsrdquo inProceedings of the IEEE International Conference on AcousticsSpeech and Signal Processing (ICASSP rsquo13) pp 1558ndash1562 May2013

[20] J Hays and A Efros ldquoScene completion using millions ofphotographsrdquo ACM Transactions on Graphics (SIGGRAPH2007) vol 26 no 3 2007

The Scientific World Journal 11

[21] O LeMeur and C Guillemot ldquoSuper-resolution-based inpaint-ingrdquo in Proceedings of European Conference on Computer Vision(ECCV rsquo12) pp 554ndash567 2012

[22] Z Xu and J Sun ldquoImage inpainting by patch propagation usingpatch sparsityrdquo IEEE Transactions on Image Processing vol 19no 5 pp 1153ndash1165 2010

[23] J Aujol S Ladjal and S Masnou ldquoExemplar-based inpaintingfrom a variational point of viewrdquo SIAM Journal onMathematicalAnalysis vol 42 no 3 pp 1246ndash1285 2010

[24] M Bertalmio L Vese G Sapiro and S Osher ldquoSimultaneousstructure and texture image inpaintingrdquo IEEE Transactions onImage Processing vol 12 no 8 pp 882ndash889 2003

[25] L Atzori and F G B de Natale ldquoError concealment in videotransmission over packet networks by a sketch-based approachrdquoSignal Processing ImageCommunication vol 15 no 1 pp 57ndash761999

[26] A Rares M J T Reinders and J Biemond ldquoEdge-based imagerestorationrdquo IEEE Transactions on Image Processing vol 14 no10 pp 1454ndash1468 2005

[27] M M Hadhoud K A Moustafa and S Z Shenoda ldquoDigitalimages inpainting using modified convolution based methodrdquoinOptical Pattern Recognition XX vol 7340 of Proceedings of theSPIE Orlando Fla USA April 2009

[28] P Perona and J Malik ldquoScale-space and edge detection usinganisotropic diffusionrdquo IEEE Transactions on Pattern Analysisand Machine Intelligence vol 12 no 7 pp 629ndash639 1990

[29] M Daisy D Tschumperle and O Lezoray ldquoA fast spatialpatch blending algorithm for artefact reduction in pattern-based image inpaintingrdquo in SIGGRAPH Asia 2013 TechnicalBriefs (SA rsquo13) pp 1ndash4 article 8 ACM New York NY USA2013

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Submit your manuscripts athttpwwwhindawicom

VLSI Design

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Civil EngineeringAdvances in

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Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

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Chemical EngineeringInternational Journal of Antennas and

Propagation

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Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

The Scientific World Journal 5

(a) (b)

(c) (d)

(e) (f)

Figure 3 Visual comparison of the proposed methods (a) simulation of a natural defect (b) the corresponding masks (c) result of theOliveira method (d) result of the proposed adaptation of Oliveira method (e) result of the Hadhoud method (f) result of the proposedadaptation of Hadhoud method

original image (without defects) does not exist thereforewe could not compute the PSNR in comparison to it Inorder to reach a conclusion regarding these methods and ourproposal for edge preserving some images were chosen anddefects were manually appliedTherefore the PSNR could becomputed by comparing the restored image with the originalone

In the image shown in Figure 3(a) we have applied adefect that could be considered close to a natural one Theblue mask will be processed using Oliveirarsquos or Hadhoudrsquosmethod as for the yellow mask an anisotropic diffusionwill be applied It can be noticed from the result in Figure 3and Table 1 that our proposal offers improvements regardingHadhoudrsquos method However it worth mentioning that theresults would be more relevant if images with natural defectswould have been tested and their originals could be used as aground truth

Table 1 PSNR values comparison for the proposed methods

Image Oliveira Hadhoud Our adaptation ofOliveira

Our adaptation ofHadhoud

Peppers 47155 42383 467605 43138Egipt 463948 438093 46038 4600067

4 An Evaluation of the Inpainting Algorithms

The five inpainting methods were implemented in the Cand run on a system with Intel i5 processor at 25 GHz Themethod proposed by Bertalmio et al was implemented onRGB color images The algorithm developed by Oliveira etal and the method proposed by Hadhoud et al were imple-mented taking into consideration the proposal describedabove regarding edge conservation In the case of Efros and

6 The Scientific World Journal

30

35

40

45

50

849 1348 1806 2636

Bertalmio et al (2000 2003)Oliviera et al (2001)Hadhoud et al (2009)

Efros and Leung (1999)Criminisi et al (2004)

Mask size

PSN

R

(a)

849 1348 1806 2636Mask size

PSN

R

30354045505560

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 4 PSNR results for (a) Lena and (b) Peepers test image

30

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

30

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

25

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 5 PSNR results for (a) Baboon and (b) StillLifeWithApples test image

Leungrsquos algorithm the source area was represented by aband around the occluded region [2] The same assumptionwas considered for the method proposed by Criminisi etal Our evaluation was carried out on representative testimages characterized by structural lines but also by texturecontent

First of all it was necessary to determine the optimalconfiguration of each method parameters in order to obtainthe best results in terms of PSNR Therefore several con-figurations for each algorithm were tested The test imagesused were Lena Peppers Baboon and StillLifeWithApplesas presented in [17] and Barbara Egipt cat fur fly helicopterand lands from [29] An artificial damage was applied and therestored image was compared to the original one as referenceOliveirarsquos method and the version proposed by Hadhoudet al were tested on the well-known inpainting test imagesLincoln and Three Girls due to their efficiency on naturaldamage images The main disadvantage was that there areno original images that could be used as reference in orderto compute the PSNR value Our artificial test damage wasdefined as a stripe successively widened in order to noticehow the algorithm behaves for ldquospot masksrdquo The data inTable 2 presents the mask (damage) size in pixels and thecorresponding initial PSNR values By gradually increasing

the mask width we had obtained the PSNR results presentedin Figures 4 5 6 7 and 8 for the ten considered test images

As it can be seen from the PSNR results among the struc-tural inpainting methods the one belonging to Bertalmioleads to the successful results among which Peppers andLena obtain the highest values Due to diffusion method thealgorithm has lower results for textural images in comparisonwith structural ones

For the last two methods there are some improvementsbut it is important to mention that in the case of texturalimages the PSNR value is not relevant as inpainting is per-formed by the replication of information from a source areaand not by actual propagation inside themask Consequentlyas the mask increases it is likely to obtain lower PSNR valuesand still have a very successful visual effect (as it can beseen from Figure 9) In the case of diffusion methods theresults are less successful leading to color spread and causingblurring effects

Considering the proposed adaptation for contour linepreserving of Oliveirarsquos and Hadhoudrsquos methods described inSection 3 an improvement has been noticed in comparisonto the basic algorithm which applied isotropic diffusion overthe entire mask Unfortunately since these two methods aresuitable for natural defects images they cannot be compared

The Scientific World Journal 7

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

55

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 6 PSNR results for (a) Barbara and (b) Egipt test image

35

40

849 1348 1806 2636Mask size

PSN

R

30

25

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

25

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 7 PSNR results for (a) cat fur and (b) fly test image

Table 2 The initial PSNR values depending on the mask size

Image Mask size (pixels)849 1348 1806 2636

Lena 3726898 349823 330214 3023914Peppers 3461765 321941 304215 278131Baboon 327125 301962 2843159 257326StillLifeWithApples 329604 307468 289399 263172Barbara 3343815 3112731 29333 266095Egipt 2994907 275565 2572072 230565Cat fur 3210376 2958541 2761433 251814Fly 30935 28547 2670904 239389Helicopter 353284 330786 3132791 287093Lands 303642 280075 262049 234996

to an original (unaltered) image In this case the PSNRvalue would be computed in comparison with other restoredimages from the literature indicating the similarity to themand the obtained values would not be a proof of a successfulrestoration There are no original images for Lincoln andThree Girls (highly referenced in the domain) therefore

a conclusive PSNR value could be determined and only avisual analysis would be possible

However the visual restoration is satisfactory as it canbe seen from Figures 10(c) and 10(e) and is processed usingour proposedmethod for edge preserving applied toOliveirarsquosand Hadhoudrsquos methods respectively In comparison withthe original Oliveira method where the obtained edge wasblurred (as shown in Figure 10(b)) our approach offers bettercontour preservation (Figure 10(c)) Also due to the kernelused in Hadhoudrsquos method the edge is altered (Figure 10(d))However applying the proposedmethod in combinationwithHadhoudrsquos leads to good visual results (Figure 10(e)) We willconclude that using our new procedure in combination withOliveirarsquos and Hadhoudrsquos methods will offer advantages inthe case of natural defects images such as Lincoln and ThreeGirls

It was found that the algorithm proposed by Bertalmioet al successfully restores images when the method isapplied to reduced surface masks or with narrow widthbecause the contour lines crossing the area can be properlyconnected The major disadvantage of the algorithm is thatfor large masks due to diffusion a blurring effect occursand therefore the algorithm fails to restore textural images

8 The Scientific World Journal

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

35404550

849 1348 1806 2636Mask size

PSN

R

30

70656055

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 8 PSNR results for (a) helicopter and (b) lands test image

(a) (b)

(c) (d)

Figure 9 Test images with damage of (a) 983 pixels (c) 2120 pixels and the corresponding results (b) and (d) obtained with Criminisirsquosalgorithm

The method however can lead to good results using smallamount of information around the mask unlike the textureinpainting algorithms which requires a more significantamount of information in order to perform the restoration

Unlike the algorithm proposed by Bertalmio et al themethod presented by Oliveira et al is less complex Howeverthis advantage fails to compensate the fact that the contourlines can be preserved only by defining the diffusion barriersand the algorithm can be successfully applied to images withnatural damageTherefore the algorithm is suitable formaskshaving narrow width otherwise a high blurring effect can benoticed

In the case of Hadhoud et al method processing timeimprovements could be noticed as a consequence of thefact that more known neighbors of the restoring pixel areused Hence the required number of iterations considerablydecreases Similarly to the Oliveira et al method the algo-rithm is suitable for restoring images that do not have highcontrast

The texture synthesis algorithm proposed by Efros andLeung led to impressive results Although in contrast toother methods the numerical values may be less satisfactorybecause the stochastic textures would be impossible torestore The restored pixels have been assigned a close value

The Scientific World Journal 9

(a) (b)

(c) (d)

(e)

Figure 10 (a) Original image (b) result for Oliveirarsquos method (c) result for our adaptation of Oliveirarsquos method (d) result for Hadhoudrsquosmethod (e) result for our adaptation of Hadhoudrsquos method

to the original one as inpainting is done by copying pixelsfromapredetermined area andnot by propagation of externalinformation The method performs well also for structuralimages but the main disadvantage consists of the extremelylong processing time caused by the pixel by pixel restoration

The Criminisi method leads to good results both forstructural and textural images since it takes into consid-eration structural information Unlike the Efros and Leungalgorithm restoration is performed block by block reducingthe processing time As a consequence a disadvantage may

occur when choosing too large blocks for replication asinappropriate information can be copied inside the occludedarea The quality of the results heavily depends on thisparameter but also on the provided context by means of asecond parameter which specifies the source bandwidth

5 Conclusions

The paper presents a comparative study regarding inpaintingtechniques in order to evaluate different types of image

10 The Scientific World Journal

restoration methods and to emphasize the advantages anddisadvantages for each of the approached algorithms

Due to the fact that a certain number of inpaintingmethods have been proposed during the last years it is stilldifficult to designate the appropriate one The algorithmschosen for our evaluation are representative for the categoriesthey belong to having as reference the first one developedby Bertalmio Other methods were also analyzed as theone proposed by Oliveira and its adapted version proposedby Hadhoud et al suitable for images without texturesRegarding these two methods an alternative to the diffusionbarriers was proposed by us The restoration of texturedimages had also been taken into account in our evaluationby using the method developed by Efros and Leung and thealgorithm proposed by Criminisi

It was also important to determine the algorithm param-eters that lead to the best PSNR results and selectingrepresentative test images to provide relevant informationThe images were restored gradually varying the width ofthe occluded area in order to analyze the influence of thisparameter The tests have shown that inpainting algorithmsinvolving diffusion operations perform well for structuralfeatures images but cannot successfully rebuild textures

Image restoration using the RGB color system for thealgorithm developed by Bertalmio led to successful resultsfor structural images The adaptation proposed for Oliveirarsquosand Hadhoudrsquos algorithms has been proven to be a successfulalternative for edge preserving with remarkable resultsHowever textural inpainting techniques are themost success-ful Even if requiring a longer processing time they performwell on both image types

Further developments of this work may consist ofimplementing hybrid methods that combine features of theapproached algorithms and comparing their results with theones belonging to the already analyzed methods Hybridmethods would require reconstruction processes for thecontour lines and restoration processes over the obtainedregions by means of textural inpainting techniques

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] P Patel A Prajapati and SMishra ldquoReviewof different inpaint-ing algorithmsrdquo International Journal of Computer Applicationsvol 59 no 18 pp 30ndash34 2012

[2] M E Taschler ldquoA comparative analysis of image inpaintingrdquoTech Rep University of York York UK 2006

[3] C Guillemot and O Le Meur ldquoImage inpainting overview andrecent advancesrdquo IEEE Signal Processing Magazine vol 31 pp127ndash144 2014

[4] M Bertalmio G Sapiro V Caselles and C Ballester ldquoImageinpaintingrdquo in Proceedings of the 27th annual conference onComputer graphics and interactive techniques (SIGGRAPH rsquo00)pp 417ndash424 July 2000

[5] A BugeauM Bertalmio V Caselles and G Sapiro ldquoA compre-hensive framework for image inpaintingrdquo IEEE Transactions onImage Processing vol 19 no 10 pp 2634ndash2645 2010

[6] T F Chan and J Shen ldquoNontexture inpainting by curvature-driven diffusionsrdquo Journal of Visual Communication and ImageRepresentation vol 12 no 4 pp 436ndash449 2001

[7] D Tschumperle and R Deriche ldquoVector-valued image regular-ization with PDEs a common framework for different appli-cationsrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 27 no 4 pp 506ndash517 2005

[8] J Sun L Yuan J Jia and H Y Shum ldquoImage completion withstructure propagationrdquo ACM Transactions on Graphics vol 24pp 861ndash868 2005

[9] M M Oliviera B Bowen R McKenna and Y S Chang ldquoFastdigital image inpaintingrdquo in Proceedings of the InternationalConference on Visualization Imaging and Image Processing(VIIP rsquo01) pp 261ndash266 2001

[10] A Telea ldquoAn image inpainting technique based on the fastmarching methodrdquo Journal of Graphics Tools vol 9 pp 23ndash342004

[11] B Yan Y Gao K Sun and B Yang ldquoEfficient seam carvingfor object removalrdquo in Proceedings of the 20th IEEE Interna-tional Conference on Image Processing (ICIP rsquo13) pp 1331ndash1335September 2013

[12] A A Efros and T K Leung ldquoTexture synthesis by non-parametric samplingrdquo in Proceedings of the 7th IEEE Interna-tional Conference onComputer Vision (ICCVrsquo99) pp 1033ndash1038Corfu Greece September 1999

[13] A A Efros and W T Freeman ldquoImage quilting for texturesynthesis and transferrdquo in Proceedings of the 28th AnnualConference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo01) pp 341ndash346 Los Angeles Calif USA August2001

[14] D J Heeger and J R Bergen ldquoPyramid-based texture anal-ysissynthesisrdquo in Proceedings of the 22nd Annual ACM Con-ference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo95) vol 29 pp 229ndash238 Los Angeles Calif USAAugust 1995

[15] J S de Bonet ldquoMultiresolution sampling procedure for analysisand synthesis of texture imagesrdquo in Proceedings of the 24thAnnual Conference on Computer Graphics and Interactive Tech-niques (SIGGRAPH rsquo97) pp 361ndash368 Los Angeles Calif USAAugust 1997

[16] H Igehy and L Pereira ldquoImage replacement through texturesynthesisrdquo in Proceedings of the International Conference onImage Processing vol 3 pp 186ndash189 Santa Barbara Calif USAOctober 1997

[17] A Criminisi P Perez and K Toyama ldquoRegion filling andobject removal by exemplar-based image inpaintingrdquo IEEETransactions on Image Processing vol 13 no 9 pp 1200ndash12122004

[18] I Drori D Cohen-Or and H Yeshurun ldquoFragmentmdashbasedimage completionrdquo ACM Transactions on Graphics vol 22 pp303ndash312 2003

[19] C Guillemot M Turkan O L Meur and M Ebdelli ldquoImageinpainting using LLE-LDNR and linear subspace mappingsrdquo inProceedings of the IEEE International Conference on AcousticsSpeech and Signal Processing (ICASSP rsquo13) pp 1558ndash1562 May2013

[20] J Hays and A Efros ldquoScene completion using millions ofphotographsrdquo ACM Transactions on Graphics (SIGGRAPH2007) vol 26 no 3 2007

The Scientific World Journal 11

[21] O LeMeur and C Guillemot ldquoSuper-resolution-based inpaint-ingrdquo in Proceedings of European Conference on Computer Vision(ECCV rsquo12) pp 554ndash567 2012

[22] Z Xu and J Sun ldquoImage inpainting by patch propagation usingpatch sparsityrdquo IEEE Transactions on Image Processing vol 19no 5 pp 1153ndash1165 2010

[23] J Aujol S Ladjal and S Masnou ldquoExemplar-based inpaintingfrom a variational point of viewrdquo SIAM Journal onMathematicalAnalysis vol 42 no 3 pp 1246ndash1285 2010

[24] M Bertalmio L Vese G Sapiro and S Osher ldquoSimultaneousstructure and texture image inpaintingrdquo IEEE Transactions onImage Processing vol 12 no 8 pp 882ndash889 2003

[25] L Atzori and F G B de Natale ldquoError concealment in videotransmission over packet networks by a sketch-based approachrdquoSignal Processing ImageCommunication vol 15 no 1 pp 57ndash761999

[26] A Rares M J T Reinders and J Biemond ldquoEdge-based imagerestorationrdquo IEEE Transactions on Image Processing vol 14 no10 pp 1454ndash1468 2005

[27] M M Hadhoud K A Moustafa and S Z Shenoda ldquoDigitalimages inpainting using modified convolution based methodrdquoinOptical Pattern Recognition XX vol 7340 of Proceedings of theSPIE Orlando Fla USA April 2009

[28] P Perona and J Malik ldquoScale-space and edge detection usinganisotropic diffusionrdquo IEEE Transactions on Pattern Analysisand Machine Intelligence vol 12 no 7 pp 629ndash639 1990

[29] M Daisy D Tschumperle and O Lezoray ldquoA fast spatialpatch blending algorithm for artefact reduction in pattern-based image inpaintingrdquo in SIGGRAPH Asia 2013 TechnicalBriefs (SA rsquo13) pp 1ndash4 article 8 ACM New York NY USA2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

6 The Scientific World Journal

30

35

40

45

50

849 1348 1806 2636

Bertalmio et al (2000 2003)Oliviera et al (2001)Hadhoud et al (2009)

Efros and Leung (1999)Criminisi et al (2004)

Mask size

PSN

R

(a)

849 1348 1806 2636Mask size

PSN

R

30354045505560

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 4 PSNR results for (a) Lena and (b) Peepers test image

30

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

30

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

25

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 5 PSNR results for (a) Baboon and (b) StillLifeWithApples test image

Leungrsquos algorithm the source area was represented by aband around the occluded region [2] The same assumptionwas considered for the method proposed by Criminisi etal Our evaluation was carried out on representative testimages characterized by structural lines but also by texturecontent

First of all it was necessary to determine the optimalconfiguration of each method parameters in order to obtainthe best results in terms of PSNR Therefore several con-figurations for each algorithm were tested The test imagesused were Lena Peppers Baboon and StillLifeWithApplesas presented in [17] and Barbara Egipt cat fur fly helicopterand lands from [29] An artificial damage was applied and therestored image was compared to the original one as referenceOliveirarsquos method and the version proposed by Hadhoudet al were tested on the well-known inpainting test imagesLincoln and Three Girls due to their efficiency on naturaldamage images The main disadvantage was that there areno original images that could be used as reference in orderto compute the PSNR value Our artificial test damage wasdefined as a stripe successively widened in order to noticehow the algorithm behaves for ldquospot masksrdquo The data inTable 2 presents the mask (damage) size in pixels and thecorresponding initial PSNR values By gradually increasing

the mask width we had obtained the PSNR results presentedin Figures 4 5 6 7 and 8 for the ten considered test images

As it can be seen from the PSNR results among the struc-tural inpainting methods the one belonging to Bertalmioleads to the successful results among which Peppers andLena obtain the highest values Due to diffusion method thealgorithm has lower results for textural images in comparisonwith structural ones

For the last two methods there are some improvementsbut it is important to mention that in the case of texturalimages the PSNR value is not relevant as inpainting is per-formed by the replication of information from a source areaand not by actual propagation inside themask Consequentlyas the mask increases it is likely to obtain lower PSNR valuesand still have a very successful visual effect (as it can beseen from Figure 9) In the case of diffusion methods theresults are less successful leading to color spread and causingblurring effects

Considering the proposed adaptation for contour linepreserving of Oliveirarsquos and Hadhoudrsquos methods described inSection 3 an improvement has been noticed in comparisonto the basic algorithm which applied isotropic diffusion overthe entire mask Unfortunately since these two methods aresuitable for natural defects images they cannot be compared

The Scientific World Journal 7

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

55

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 6 PSNR results for (a) Barbara and (b) Egipt test image

35

40

849 1348 1806 2636Mask size

PSN

R

30

25

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

25

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 7 PSNR results for (a) cat fur and (b) fly test image

Table 2 The initial PSNR values depending on the mask size

Image Mask size (pixels)849 1348 1806 2636

Lena 3726898 349823 330214 3023914Peppers 3461765 321941 304215 278131Baboon 327125 301962 2843159 257326StillLifeWithApples 329604 307468 289399 263172Barbara 3343815 3112731 29333 266095Egipt 2994907 275565 2572072 230565Cat fur 3210376 2958541 2761433 251814Fly 30935 28547 2670904 239389Helicopter 353284 330786 3132791 287093Lands 303642 280075 262049 234996

to an original (unaltered) image In this case the PSNRvalue would be computed in comparison with other restoredimages from the literature indicating the similarity to themand the obtained values would not be a proof of a successfulrestoration There are no original images for Lincoln andThree Girls (highly referenced in the domain) therefore

a conclusive PSNR value could be determined and only avisual analysis would be possible

However the visual restoration is satisfactory as it canbe seen from Figures 10(c) and 10(e) and is processed usingour proposedmethod for edge preserving applied toOliveirarsquosand Hadhoudrsquos methods respectively In comparison withthe original Oliveira method where the obtained edge wasblurred (as shown in Figure 10(b)) our approach offers bettercontour preservation (Figure 10(c)) Also due to the kernelused in Hadhoudrsquos method the edge is altered (Figure 10(d))However applying the proposedmethod in combinationwithHadhoudrsquos leads to good visual results (Figure 10(e)) We willconclude that using our new procedure in combination withOliveirarsquos and Hadhoudrsquos methods will offer advantages inthe case of natural defects images such as Lincoln and ThreeGirls

It was found that the algorithm proposed by Bertalmioet al successfully restores images when the method isapplied to reduced surface masks or with narrow widthbecause the contour lines crossing the area can be properlyconnected The major disadvantage of the algorithm is thatfor large masks due to diffusion a blurring effect occursand therefore the algorithm fails to restore textural images

8 The Scientific World Journal

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

35404550

849 1348 1806 2636Mask size

PSN

R

30

70656055

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 8 PSNR results for (a) helicopter and (b) lands test image

(a) (b)

(c) (d)

Figure 9 Test images with damage of (a) 983 pixels (c) 2120 pixels and the corresponding results (b) and (d) obtained with Criminisirsquosalgorithm

The method however can lead to good results using smallamount of information around the mask unlike the textureinpainting algorithms which requires a more significantamount of information in order to perform the restoration

Unlike the algorithm proposed by Bertalmio et al themethod presented by Oliveira et al is less complex Howeverthis advantage fails to compensate the fact that the contourlines can be preserved only by defining the diffusion barriersand the algorithm can be successfully applied to images withnatural damageTherefore the algorithm is suitable formaskshaving narrow width otherwise a high blurring effect can benoticed

In the case of Hadhoud et al method processing timeimprovements could be noticed as a consequence of thefact that more known neighbors of the restoring pixel areused Hence the required number of iterations considerablydecreases Similarly to the Oliveira et al method the algo-rithm is suitable for restoring images that do not have highcontrast

The texture synthesis algorithm proposed by Efros andLeung led to impressive results Although in contrast toother methods the numerical values may be less satisfactorybecause the stochastic textures would be impossible torestore The restored pixels have been assigned a close value

The Scientific World Journal 9

(a) (b)

(c) (d)

(e)

Figure 10 (a) Original image (b) result for Oliveirarsquos method (c) result for our adaptation of Oliveirarsquos method (d) result for Hadhoudrsquosmethod (e) result for our adaptation of Hadhoudrsquos method

to the original one as inpainting is done by copying pixelsfromapredetermined area andnot by propagation of externalinformation The method performs well also for structuralimages but the main disadvantage consists of the extremelylong processing time caused by the pixel by pixel restoration

The Criminisi method leads to good results both forstructural and textural images since it takes into consid-eration structural information Unlike the Efros and Leungalgorithm restoration is performed block by block reducingthe processing time As a consequence a disadvantage may

occur when choosing too large blocks for replication asinappropriate information can be copied inside the occludedarea The quality of the results heavily depends on thisparameter but also on the provided context by means of asecond parameter which specifies the source bandwidth

5 Conclusions

The paper presents a comparative study regarding inpaintingtechniques in order to evaluate different types of image

10 The Scientific World Journal

restoration methods and to emphasize the advantages anddisadvantages for each of the approached algorithms

Due to the fact that a certain number of inpaintingmethods have been proposed during the last years it is stilldifficult to designate the appropriate one The algorithmschosen for our evaluation are representative for the categoriesthey belong to having as reference the first one developedby Bertalmio Other methods were also analyzed as theone proposed by Oliveira and its adapted version proposedby Hadhoud et al suitable for images without texturesRegarding these two methods an alternative to the diffusionbarriers was proposed by us The restoration of texturedimages had also been taken into account in our evaluationby using the method developed by Efros and Leung and thealgorithm proposed by Criminisi

It was also important to determine the algorithm param-eters that lead to the best PSNR results and selectingrepresentative test images to provide relevant informationThe images were restored gradually varying the width ofthe occluded area in order to analyze the influence of thisparameter The tests have shown that inpainting algorithmsinvolving diffusion operations perform well for structuralfeatures images but cannot successfully rebuild textures

Image restoration using the RGB color system for thealgorithm developed by Bertalmio led to successful resultsfor structural images The adaptation proposed for Oliveirarsquosand Hadhoudrsquos algorithms has been proven to be a successfulalternative for edge preserving with remarkable resultsHowever textural inpainting techniques are themost success-ful Even if requiring a longer processing time they performwell on both image types

Further developments of this work may consist ofimplementing hybrid methods that combine features of theapproached algorithms and comparing their results with theones belonging to the already analyzed methods Hybridmethods would require reconstruction processes for thecontour lines and restoration processes over the obtainedregions by means of textural inpainting techniques

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] P Patel A Prajapati and SMishra ldquoReviewof different inpaint-ing algorithmsrdquo International Journal of Computer Applicationsvol 59 no 18 pp 30ndash34 2012

[2] M E Taschler ldquoA comparative analysis of image inpaintingrdquoTech Rep University of York York UK 2006

[3] C Guillemot and O Le Meur ldquoImage inpainting overview andrecent advancesrdquo IEEE Signal Processing Magazine vol 31 pp127ndash144 2014

[4] M Bertalmio G Sapiro V Caselles and C Ballester ldquoImageinpaintingrdquo in Proceedings of the 27th annual conference onComputer graphics and interactive techniques (SIGGRAPH rsquo00)pp 417ndash424 July 2000

[5] A BugeauM Bertalmio V Caselles and G Sapiro ldquoA compre-hensive framework for image inpaintingrdquo IEEE Transactions onImage Processing vol 19 no 10 pp 2634ndash2645 2010

[6] T F Chan and J Shen ldquoNontexture inpainting by curvature-driven diffusionsrdquo Journal of Visual Communication and ImageRepresentation vol 12 no 4 pp 436ndash449 2001

[7] D Tschumperle and R Deriche ldquoVector-valued image regular-ization with PDEs a common framework for different appli-cationsrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 27 no 4 pp 506ndash517 2005

[8] J Sun L Yuan J Jia and H Y Shum ldquoImage completion withstructure propagationrdquo ACM Transactions on Graphics vol 24pp 861ndash868 2005

[9] M M Oliviera B Bowen R McKenna and Y S Chang ldquoFastdigital image inpaintingrdquo in Proceedings of the InternationalConference on Visualization Imaging and Image Processing(VIIP rsquo01) pp 261ndash266 2001

[10] A Telea ldquoAn image inpainting technique based on the fastmarching methodrdquo Journal of Graphics Tools vol 9 pp 23ndash342004

[11] B Yan Y Gao K Sun and B Yang ldquoEfficient seam carvingfor object removalrdquo in Proceedings of the 20th IEEE Interna-tional Conference on Image Processing (ICIP rsquo13) pp 1331ndash1335September 2013

[12] A A Efros and T K Leung ldquoTexture synthesis by non-parametric samplingrdquo in Proceedings of the 7th IEEE Interna-tional Conference onComputer Vision (ICCVrsquo99) pp 1033ndash1038Corfu Greece September 1999

[13] A A Efros and W T Freeman ldquoImage quilting for texturesynthesis and transferrdquo in Proceedings of the 28th AnnualConference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo01) pp 341ndash346 Los Angeles Calif USA August2001

[14] D J Heeger and J R Bergen ldquoPyramid-based texture anal-ysissynthesisrdquo in Proceedings of the 22nd Annual ACM Con-ference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo95) vol 29 pp 229ndash238 Los Angeles Calif USAAugust 1995

[15] J S de Bonet ldquoMultiresolution sampling procedure for analysisand synthesis of texture imagesrdquo in Proceedings of the 24thAnnual Conference on Computer Graphics and Interactive Tech-niques (SIGGRAPH rsquo97) pp 361ndash368 Los Angeles Calif USAAugust 1997

[16] H Igehy and L Pereira ldquoImage replacement through texturesynthesisrdquo in Proceedings of the International Conference onImage Processing vol 3 pp 186ndash189 Santa Barbara Calif USAOctober 1997

[17] A Criminisi P Perez and K Toyama ldquoRegion filling andobject removal by exemplar-based image inpaintingrdquo IEEETransactions on Image Processing vol 13 no 9 pp 1200ndash12122004

[18] I Drori D Cohen-Or and H Yeshurun ldquoFragmentmdashbasedimage completionrdquo ACM Transactions on Graphics vol 22 pp303ndash312 2003

[19] C Guillemot M Turkan O L Meur and M Ebdelli ldquoImageinpainting using LLE-LDNR and linear subspace mappingsrdquo inProceedings of the IEEE International Conference on AcousticsSpeech and Signal Processing (ICASSP rsquo13) pp 1558ndash1562 May2013

[20] J Hays and A Efros ldquoScene completion using millions ofphotographsrdquo ACM Transactions on Graphics (SIGGRAPH2007) vol 26 no 3 2007

The Scientific World Journal 11

[21] O LeMeur and C Guillemot ldquoSuper-resolution-based inpaint-ingrdquo in Proceedings of European Conference on Computer Vision(ECCV rsquo12) pp 554ndash567 2012

[22] Z Xu and J Sun ldquoImage inpainting by patch propagation usingpatch sparsityrdquo IEEE Transactions on Image Processing vol 19no 5 pp 1153ndash1165 2010

[23] J Aujol S Ladjal and S Masnou ldquoExemplar-based inpaintingfrom a variational point of viewrdquo SIAM Journal onMathematicalAnalysis vol 42 no 3 pp 1246ndash1285 2010

[24] M Bertalmio L Vese G Sapiro and S Osher ldquoSimultaneousstructure and texture image inpaintingrdquo IEEE Transactions onImage Processing vol 12 no 8 pp 882ndash889 2003

[25] L Atzori and F G B de Natale ldquoError concealment in videotransmission over packet networks by a sketch-based approachrdquoSignal Processing ImageCommunication vol 15 no 1 pp 57ndash761999

[26] A Rares M J T Reinders and J Biemond ldquoEdge-based imagerestorationrdquo IEEE Transactions on Image Processing vol 14 no10 pp 1454ndash1468 2005

[27] M M Hadhoud K A Moustafa and S Z Shenoda ldquoDigitalimages inpainting using modified convolution based methodrdquoinOptical Pattern Recognition XX vol 7340 of Proceedings of theSPIE Orlando Fla USA April 2009

[28] P Perona and J Malik ldquoScale-space and edge detection usinganisotropic diffusionrdquo IEEE Transactions on Pattern Analysisand Machine Intelligence vol 12 no 7 pp 629ndash639 1990

[29] M Daisy D Tschumperle and O Lezoray ldquoA fast spatialpatch blending algorithm for artefact reduction in pattern-based image inpaintingrdquo in SIGGRAPH Asia 2013 TechnicalBriefs (SA rsquo13) pp 1ndash4 article 8 ACM New York NY USA2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

The Scientific World Journal 7

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

55

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 6 PSNR results for (a) Barbara and (b) Egipt test image

35

40

849 1348 1806 2636Mask size

PSN

R

30

25

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

25

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 7 PSNR results for (a) cat fur and (b) fly test image

Table 2 The initial PSNR values depending on the mask size

Image Mask size (pixels)849 1348 1806 2636

Lena 3726898 349823 330214 3023914Peppers 3461765 321941 304215 278131Baboon 327125 301962 2843159 257326StillLifeWithApples 329604 307468 289399 263172Barbara 3343815 3112731 29333 266095Egipt 2994907 275565 2572072 230565Cat fur 3210376 2958541 2761433 251814Fly 30935 28547 2670904 239389Helicopter 353284 330786 3132791 287093Lands 303642 280075 262049 234996

to an original (unaltered) image In this case the PSNRvalue would be computed in comparison with other restoredimages from the literature indicating the similarity to themand the obtained values would not be a proof of a successfulrestoration There are no original images for Lincoln andThree Girls (highly referenced in the domain) therefore

a conclusive PSNR value could be determined and only avisual analysis would be possible

However the visual restoration is satisfactory as it canbe seen from Figures 10(c) and 10(e) and is processed usingour proposedmethod for edge preserving applied toOliveirarsquosand Hadhoudrsquos methods respectively In comparison withthe original Oliveira method where the obtained edge wasblurred (as shown in Figure 10(b)) our approach offers bettercontour preservation (Figure 10(c)) Also due to the kernelused in Hadhoudrsquos method the edge is altered (Figure 10(d))However applying the proposedmethod in combinationwithHadhoudrsquos leads to good visual results (Figure 10(e)) We willconclude that using our new procedure in combination withOliveirarsquos and Hadhoudrsquos methods will offer advantages inthe case of natural defects images such as Lincoln and ThreeGirls

It was found that the algorithm proposed by Bertalmioet al successfully restores images when the method isapplied to reduced surface masks or with narrow widthbecause the contour lines crossing the area can be properlyconnected The major disadvantage of the algorithm is thatfor large masks due to diffusion a blurring effect occursand therefore the algorithm fails to restore textural images

8 The Scientific World Journal

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

35404550

849 1348 1806 2636Mask size

PSN

R

30

70656055

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 8 PSNR results for (a) helicopter and (b) lands test image

(a) (b)

(c) (d)

Figure 9 Test images with damage of (a) 983 pixels (c) 2120 pixels and the corresponding results (b) and (d) obtained with Criminisirsquosalgorithm

The method however can lead to good results using smallamount of information around the mask unlike the textureinpainting algorithms which requires a more significantamount of information in order to perform the restoration

Unlike the algorithm proposed by Bertalmio et al themethod presented by Oliveira et al is less complex Howeverthis advantage fails to compensate the fact that the contourlines can be preserved only by defining the diffusion barriersand the algorithm can be successfully applied to images withnatural damageTherefore the algorithm is suitable formaskshaving narrow width otherwise a high blurring effect can benoticed

In the case of Hadhoud et al method processing timeimprovements could be noticed as a consequence of thefact that more known neighbors of the restoring pixel areused Hence the required number of iterations considerablydecreases Similarly to the Oliveira et al method the algo-rithm is suitable for restoring images that do not have highcontrast

The texture synthesis algorithm proposed by Efros andLeung led to impressive results Although in contrast toother methods the numerical values may be less satisfactorybecause the stochastic textures would be impossible torestore The restored pixels have been assigned a close value

The Scientific World Journal 9

(a) (b)

(c) (d)

(e)

Figure 10 (a) Original image (b) result for Oliveirarsquos method (c) result for our adaptation of Oliveirarsquos method (d) result for Hadhoudrsquosmethod (e) result for our adaptation of Hadhoudrsquos method

to the original one as inpainting is done by copying pixelsfromapredetermined area andnot by propagation of externalinformation The method performs well also for structuralimages but the main disadvantage consists of the extremelylong processing time caused by the pixel by pixel restoration

The Criminisi method leads to good results both forstructural and textural images since it takes into consid-eration structural information Unlike the Efros and Leungalgorithm restoration is performed block by block reducingthe processing time As a consequence a disadvantage may

occur when choosing too large blocks for replication asinappropriate information can be copied inside the occludedarea The quality of the results heavily depends on thisparameter but also on the provided context by means of asecond parameter which specifies the source bandwidth

5 Conclusions

The paper presents a comparative study regarding inpaintingtechniques in order to evaluate different types of image

10 The Scientific World Journal

restoration methods and to emphasize the advantages anddisadvantages for each of the approached algorithms

Due to the fact that a certain number of inpaintingmethods have been proposed during the last years it is stilldifficult to designate the appropriate one The algorithmschosen for our evaluation are representative for the categoriesthey belong to having as reference the first one developedby Bertalmio Other methods were also analyzed as theone proposed by Oliveira and its adapted version proposedby Hadhoud et al suitable for images without texturesRegarding these two methods an alternative to the diffusionbarriers was proposed by us The restoration of texturedimages had also been taken into account in our evaluationby using the method developed by Efros and Leung and thealgorithm proposed by Criminisi

It was also important to determine the algorithm param-eters that lead to the best PSNR results and selectingrepresentative test images to provide relevant informationThe images were restored gradually varying the width ofthe occluded area in order to analyze the influence of thisparameter The tests have shown that inpainting algorithmsinvolving diffusion operations perform well for structuralfeatures images but cannot successfully rebuild textures

Image restoration using the RGB color system for thealgorithm developed by Bertalmio led to successful resultsfor structural images The adaptation proposed for Oliveirarsquosand Hadhoudrsquos algorithms has been proven to be a successfulalternative for edge preserving with remarkable resultsHowever textural inpainting techniques are themost success-ful Even if requiring a longer processing time they performwell on both image types

Further developments of this work may consist ofimplementing hybrid methods that combine features of theapproached algorithms and comparing their results with theones belonging to the already analyzed methods Hybridmethods would require reconstruction processes for thecontour lines and restoration processes over the obtainedregions by means of textural inpainting techniques

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] P Patel A Prajapati and SMishra ldquoReviewof different inpaint-ing algorithmsrdquo International Journal of Computer Applicationsvol 59 no 18 pp 30ndash34 2012

[2] M E Taschler ldquoA comparative analysis of image inpaintingrdquoTech Rep University of York York UK 2006

[3] C Guillemot and O Le Meur ldquoImage inpainting overview andrecent advancesrdquo IEEE Signal Processing Magazine vol 31 pp127ndash144 2014

[4] M Bertalmio G Sapiro V Caselles and C Ballester ldquoImageinpaintingrdquo in Proceedings of the 27th annual conference onComputer graphics and interactive techniques (SIGGRAPH rsquo00)pp 417ndash424 July 2000

[5] A BugeauM Bertalmio V Caselles and G Sapiro ldquoA compre-hensive framework for image inpaintingrdquo IEEE Transactions onImage Processing vol 19 no 10 pp 2634ndash2645 2010

[6] T F Chan and J Shen ldquoNontexture inpainting by curvature-driven diffusionsrdquo Journal of Visual Communication and ImageRepresentation vol 12 no 4 pp 436ndash449 2001

[7] D Tschumperle and R Deriche ldquoVector-valued image regular-ization with PDEs a common framework for different appli-cationsrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 27 no 4 pp 506ndash517 2005

[8] J Sun L Yuan J Jia and H Y Shum ldquoImage completion withstructure propagationrdquo ACM Transactions on Graphics vol 24pp 861ndash868 2005

[9] M M Oliviera B Bowen R McKenna and Y S Chang ldquoFastdigital image inpaintingrdquo in Proceedings of the InternationalConference on Visualization Imaging and Image Processing(VIIP rsquo01) pp 261ndash266 2001

[10] A Telea ldquoAn image inpainting technique based on the fastmarching methodrdquo Journal of Graphics Tools vol 9 pp 23ndash342004

[11] B Yan Y Gao K Sun and B Yang ldquoEfficient seam carvingfor object removalrdquo in Proceedings of the 20th IEEE Interna-tional Conference on Image Processing (ICIP rsquo13) pp 1331ndash1335September 2013

[12] A A Efros and T K Leung ldquoTexture synthesis by non-parametric samplingrdquo in Proceedings of the 7th IEEE Interna-tional Conference onComputer Vision (ICCVrsquo99) pp 1033ndash1038Corfu Greece September 1999

[13] A A Efros and W T Freeman ldquoImage quilting for texturesynthesis and transferrdquo in Proceedings of the 28th AnnualConference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo01) pp 341ndash346 Los Angeles Calif USA August2001

[14] D J Heeger and J R Bergen ldquoPyramid-based texture anal-ysissynthesisrdquo in Proceedings of the 22nd Annual ACM Con-ference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo95) vol 29 pp 229ndash238 Los Angeles Calif USAAugust 1995

[15] J S de Bonet ldquoMultiresolution sampling procedure for analysisand synthesis of texture imagesrdquo in Proceedings of the 24thAnnual Conference on Computer Graphics and Interactive Tech-niques (SIGGRAPH rsquo97) pp 361ndash368 Los Angeles Calif USAAugust 1997

[16] H Igehy and L Pereira ldquoImage replacement through texturesynthesisrdquo in Proceedings of the International Conference onImage Processing vol 3 pp 186ndash189 Santa Barbara Calif USAOctober 1997

[17] A Criminisi P Perez and K Toyama ldquoRegion filling andobject removal by exemplar-based image inpaintingrdquo IEEETransactions on Image Processing vol 13 no 9 pp 1200ndash12122004

[18] I Drori D Cohen-Or and H Yeshurun ldquoFragmentmdashbasedimage completionrdquo ACM Transactions on Graphics vol 22 pp303ndash312 2003

[19] C Guillemot M Turkan O L Meur and M Ebdelli ldquoImageinpainting using LLE-LDNR and linear subspace mappingsrdquo inProceedings of the IEEE International Conference on AcousticsSpeech and Signal Processing (ICASSP rsquo13) pp 1558ndash1562 May2013

[20] J Hays and A Efros ldquoScene completion using millions ofphotographsrdquo ACM Transactions on Graphics (SIGGRAPH2007) vol 26 no 3 2007

The Scientific World Journal 11

[21] O LeMeur and C Guillemot ldquoSuper-resolution-based inpaint-ingrdquo in Proceedings of European Conference on Computer Vision(ECCV rsquo12) pp 554ndash567 2012

[22] Z Xu and J Sun ldquoImage inpainting by patch propagation usingpatch sparsityrdquo IEEE Transactions on Image Processing vol 19no 5 pp 1153ndash1165 2010

[23] J Aujol S Ladjal and S Masnou ldquoExemplar-based inpaintingfrom a variational point of viewrdquo SIAM Journal onMathematicalAnalysis vol 42 no 3 pp 1246ndash1285 2010

[24] M Bertalmio L Vese G Sapiro and S Osher ldquoSimultaneousstructure and texture image inpaintingrdquo IEEE Transactions onImage Processing vol 12 no 8 pp 882ndash889 2003

[25] L Atzori and F G B de Natale ldquoError concealment in videotransmission over packet networks by a sketch-based approachrdquoSignal Processing ImageCommunication vol 15 no 1 pp 57ndash761999

[26] A Rares M J T Reinders and J Biemond ldquoEdge-based imagerestorationrdquo IEEE Transactions on Image Processing vol 14 no10 pp 1454ndash1468 2005

[27] M M Hadhoud K A Moustafa and S Z Shenoda ldquoDigitalimages inpainting using modified convolution based methodrdquoinOptical Pattern Recognition XX vol 7340 of Proceedings of theSPIE Orlando Fla USA April 2009

[28] P Perona and J Malik ldquoScale-space and edge detection usinganisotropic diffusionrdquo IEEE Transactions on Pattern Analysisand Machine Intelligence vol 12 no 7 pp 629ndash639 1990

[29] M Daisy D Tschumperle and O Lezoray ldquoA fast spatialpatch blending algorithm for artefact reduction in pattern-based image inpaintingrdquo in SIGGRAPH Asia 2013 TechnicalBriefs (SA rsquo13) pp 1ndash4 article 8 ACM New York NY USA2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

8 The Scientific World Journal

35

40

45

50

849 1348 1806 2636Mask size

PSN

R

30

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(a)

35404550

849 1348 1806 2636Mask size

PSN

R

30

70656055

Bertalmio et al (2000 2003) Efros and Leung (1999)Criminisi et al (2004)Oliviera et al (2001)

Hadhoud et al (2009)

(b)

Figure 8 PSNR results for (a) helicopter and (b) lands test image

(a) (b)

(c) (d)

Figure 9 Test images with damage of (a) 983 pixels (c) 2120 pixels and the corresponding results (b) and (d) obtained with Criminisirsquosalgorithm

The method however can lead to good results using smallamount of information around the mask unlike the textureinpainting algorithms which requires a more significantamount of information in order to perform the restoration

Unlike the algorithm proposed by Bertalmio et al themethod presented by Oliveira et al is less complex Howeverthis advantage fails to compensate the fact that the contourlines can be preserved only by defining the diffusion barriersand the algorithm can be successfully applied to images withnatural damageTherefore the algorithm is suitable formaskshaving narrow width otherwise a high blurring effect can benoticed

In the case of Hadhoud et al method processing timeimprovements could be noticed as a consequence of thefact that more known neighbors of the restoring pixel areused Hence the required number of iterations considerablydecreases Similarly to the Oliveira et al method the algo-rithm is suitable for restoring images that do not have highcontrast

The texture synthesis algorithm proposed by Efros andLeung led to impressive results Although in contrast toother methods the numerical values may be less satisfactorybecause the stochastic textures would be impossible torestore The restored pixels have been assigned a close value

The Scientific World Journal 9

(a) (b)

(c) (d)

(e)

Figure 10 (a) Original image (b) result for Oliveirarsquos method (c) result for our adaptation of Oliveirarsquos method (d) result for Hadhoudrsquosmethod (e) result for our adaptation of Hadhoudrsquos method

to the original one as inpainting is done by copying pixelsfromapredetermined area andnot by propagation of externalinformation The method performs well also for structuralimages but the main disadvantage consists of the extremelylong processing time caused by the pixel by pixel restoration

The Criminisi method leads to good results both forstructural and textural images since it takes into consid-eration structural information Unlike the Efros and Leungalgorithm restoration is performed block by block reducingthe processing time As a consequence a disadvantage may

occur when choosing too large blocks for replication asinappropriate information can be copied inside the occludedarea The quality of the results heavily depends on thisparameter but also on the provided context by means of asecond parameter which specifies the source bandwidth

5 Conclusions

The paper presents a comparative study regarding inpaintingtechniques in order to evaluate different types of image

10 The Scientific World Journal

restoration methods and to emphasize the advantages anddisadvantages for each of the approached algorithms

Due to the fact that a certain number of inpaintingmethods have been proposed during the last years it is stilldifficult to designate the appropriate one The algorithmschosen for our evaluation are representative for the categoriesthey belong to having as reference the first one developedby Bertalmio Other methods were also analyzed as theone proposed by Oliveira and its adapted version proposedby Hadhoud et al suitable for images without texturesRegarding these two methods an alternative to the diffusionbarriers was proposed by us The restoration of texturedimages had also been taken into account in our evaluationby using the method developed by Efros and Leung and thealgorithm proposed by Criminisi

It was also important to determine the algorithm param-eters that lead to the best PSNR results and selectingrepresentative test images to provide relevant informationThe images were restored gradually varying the width ofthe occluded area in order to analyze the influence of thisparameter The tests have shown that inpainting algorithmsinvolving diffusion operations perform well for structuralfeatures images but cannot successfully rebuild textures

Image restoration using the RGB color system for thealgorithm developed by Bertalmio led to successful resultsfor structural images The adaptation proposed for Oliveirarsquosand Hadhoudrsquos algorithms has been proven to be a successfulalternative for edge preserving with remarkable resultsHowever textural inpainting techniques are themost success-ful Even if requiring a longer processing time they performwell on both image types

Further developments of this work may consist ofimplementing hybrid methods that combine features of theapproached algorithms and comparing their results with theones belonging to the already analyzed methods Hybridmethods would require reconstruction processes for thecontour lines and restoration processes over the obtainedregions by means of textural inpainting techniques

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] P Patel A Prajapati and SMishra ldquoReviewof different inpaint-ing algorithmsrdquo International Journal of Computer Applicationsvol 59 no 18 pp 30ndash34 2012

[2] M E Taschler ldquoA comparative analysis of image inpaintingrdquoTech Rep University of York York UK 2006

[3] C Guillemot and O Le Meur ldquoImage inpainting overview andrecent advancesrdquo IEEE Signal Processing Magazine vol 31 pp127ndash144 2014

[4] M Bertalmio G Sapiro V Caselles and C Ballester ldquoImageinpaintingrdquo in Proceedings of the 27th annual conference onComputer graphics and interactive techniques (SIGGRAPH rsquo00)pp 417ndash424 July 2000

[5] A BugeauM Bertalmio V Caselles and G Sapiro ldquoA compre-hensive framework for image inpaintingrdquo IEEE Transactions onImage Processing vol 19 no 10 pp 2634ndash2645 2010

[6] T F Chan and J Shen ldquoNontexture inpainting by curvature-driven diffusionsrdquo Journal of Visual Communication and ImageRepresentation vol 12 no 4 pp 436ndash449 2001

[7] D Tschumperle and R Deriche ldquoVector-valued image regular-ization with PDEs a common framework for different appli-cationsrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 27 no 4 pp 506ndash517 2005

[8] J Sun L Yuan J Jia and H Y Shum ldquoImage completion withstructure propagationrdquo ACM Transactions on Graphics vol 24pp 861ndash868 2005

[9] M M Oliviera B Bowen R McKenna and Y S Chang ldquoFastdigital image inpaintingrdquo in Proceedings of the InternationalConference on Visualization Imaging and Image Processing(VIIP rsquo01) pp 261ndash266 2001

[10] A Telea ldquoAn image inpainting technique based on the fastmarching methodrdquo Journal of Graphics Tools vol 9 pp 23ndash342004

[11] B Yan Y Gao K Sun and B Yang ldquoEfficient seam carvingfor object removalrdquo in Proceedings of the 20th IEEE Interna-tional Conference on Image Processing (ICIP rsquo13) pp 1331ndash1335September 2013

[12] A A Efros and T K Leung ldquoTexture synthesis by non-parametric samplingrdquo in Proceedings of the 7th IEEE Interna-tional Conference onComputer Vision (ICCVrsquo99) pp 1033ndash1038Corfu Greece September 1999

[13] A A Efros and W T Freeman ldquoImage quilting for texturesynthesis and transferrdquo in Proceedings of the 28th AnnualConference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo01) pp 341ndash346 Los Angeles Calif USA August2001

[14] D J Heeger and J R Bergen ldquoPyramid-based texture anal-ysissynthesisrdquo in Proceedings of the 22nd Annual ACM Con-ference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo95) vol 29 pp 229ndash238 Los Angeles Calif USAAugust 1995

[15] J S de Bonet ldquoMultiresolution sampling procedure for analysisand synthesis of texture imagesrdquo in Proceedings of the 24thAnnual Conference on Computer Graphics and Interactive Tech-niques (SIGGRAPH rsquo97) pp 361ndash368 Los Angeles Calif USAAugust 1997

[16] H Igehy and L Pereira ldquoImage replacement through texturesynthesisrdquo in Proceedings of the International Conference onImage Processing vol 3 pp 186ndash189 Santa Barbara Calif USAOctober 1997

[17] A Criminisi P Perez and K Toyama ldquoRegion filling andobject removal by exemplar-based image inpaintingrdquo IEEETransactions on Image Processing vol 13 no 9 pp 1200ndash12122004

[18] I Drori D Cohen-Or and H Yeshurun ldquoFragmentmdashbasedimage completionrdquo ACM Transactions on Graphics vol 22 pp303ndash312 2003

[19] C Guillemot M Turkan O L Meur and M Ebdelli ldquoImageinpainting using LLE-LDNR and linear subspace mappingsrdquo inProceedings of the IEEE International Conference on AcousticsSpeech and Signal Processing (ICASSP rsquo13) pp 1558ndash1562 May2013

[20] J Hays and A Efros ldquoScene completion using millions ofphotographsrdquo ACM Transactions on Graphics (SIGGRAPH2007) vol 26 no 3 2007

The Scientific World Journal 11

[21] O LeMeur and C Guillemot ldquoSuper-resolution-based inpaint-ingrdquo in Proceedings of European Conference on Computer Vision(ECCV rsquo12) pp 554ndash567 2012

[22] Z Xu and J Sun ldquoImage inpainting by patch propagation usingpatch sparsityrdquo IEEE Transactions on Image Processing vol 19no 5 pp 1153ndash1165 2010

[23] J Aujol S Ladjal and S Masnou ldquoExemplar-based inpaintingfrom a variational point of viewrdquo SIAM Journal onMathematicalAnalysis vol 42 no 3 pp 1246ndash1285 2010

[24] M Bertalmio L Vese G Sapiro and S Osher ldquoSimultaneousstructure and texture image inpaintingrdquo IEEE Transactions onImage Processing vol 12 no 8 pp 882ndash889 2003

[25] L Atzori and F G B de Natale ldquoError concealment in videotransmission over packet networks by a sketch-based approachrdquoSignal Processing ImageCommunication vol 15 no 1 pp 57ndash761999

[26] A Rares M J T Reinders and J Biemond ldquoEdge-based imagerestorationrdquo IEEE Transactions on Image Processing vol 14 no10 pp 1454ndash1468 2005

[27] M M Hadhoud K A Moustafa and S Z Shenoda ldquoDigitalimages inpainting using modified convolution based methodrdquoinOptical Pattern Recognition XX vol 7340 of Proceedings of theSPIE Orlando Fla USA April 2009

[28] P Perona and J Malik ldquoScale-space and edge detection usinganisotropic diffusionrdquo IEEE Transactions on Pattern Analysisand Machine Intelligence vol 12 no 7 pp 629ndash639 1990

[29] M Daisy D Tschumperle and O Lezoray ldquoA fast spatialpatch blending algorithm for artefact reduction in pattern-based image inpaintingrdquo in SIGGRAPH Asia 2013 TechnicalBriefs (SA rsquo13) pp 1ndash4 article 8 ACM New York NY USA2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

The Scientific World Journal 9

(a) (b)

(c) (d)

(e)

Figure 10 (a) Original image (b) result for Oliveirarsquos method (c) result for our adaptation of Oliveirarsquos method (d) result for Hadhoudrsquosmethod (e) result for our adaptation of Hadhoudrsquos method

to the original one as inpainting is done by copying pixelsfromapredetermined area andnot by propagation of externalinformation The method performs well also for structuralimages but the main disadvantage consists of the extremelylong processing time caused by the pixel by pixel restoration

The Criminisi method leads to good results both forstructural and textural images since it takes into consid-eration structural information Unlike the Efros and Leungalgorithm restoration is performed block by block reducingthe processing time As a consequence a disadvantage may

occur when choosing too large blocks for replication asinappropriate information can be copied inside the occludedarea The quality of the results heavily depends on thisparameter but also on the provided context by means of asecond parameter which specifies the source bandwidth

5 Conclusions

The paper presents a comparative study regarding inpaintingtechniques in order to evaluate different types of image

10 The Scientific World Journal

restoration methods and to emphasize the advantages anddisadvantages for each of the approached algorithms

Due to the fact that a certain number of inpaintingmethods have been proposed during the last years it is stilldifficult to designate the appropriate one The algorithmschosen for our evaluation are representative for the categoriesthey belong to having as reference the first one developedby Bertalmio Other methods were also analyzed as theone proposed by Oliveira and its adapted version proposedby Hadhoud et al suitable for images without texturesRegarding these two methods an alternative to the diffusionbarriers was proposed by us The restoration of texturedimages had also been taken into account in our evaluationby using the method developed by Efros and Leung and thealgorithm proposed by Criminisi

It was also important to determine the algorithm param-eters that lead to the best PSNR results and selectingrepresentative test images to provide relevant informationThe images were restored gradually varying the width ofthe occluded area in order to analyze the influence of thisparameter The tests have shown that inpainting algorithmsinvolving diffusion operations perform well for structuralfeatures images but cannot successfully rebuild textures

Image restoration using the RGB color system for thealgorithm developed by Bertalmio led to successful resultsfor structural images The adaptation proposed for Oliveirarsquosand Hadhoudrsquos algorithms has been proven to be a successfulalternative for edge preserving with remarkable resultsHowever textural inpainting techniques are themost success-ful Even if requiring a longer processing time they performwell on both image types

Further developments of this work may consist ofimplementing hybrid methods that combine features of theapproached algorithms and comparing their results with theones belonging to the already analyzed methods Hybridmethods would require reconstruction processes for thecontour lines and restoration processes over the obtainedregions by means of textural inpainting techniques

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] P Patel A Prajapati and SMishra ldquoReviewof different inpaint-ing algorithmsrdquo International Journal of Computer Applicationsvol 59 no 18 pp 30ndash34 2012

[2] M E Taschler ldquoA comparative analysis of image inpaintingrdquoTech Rep University of York York UK 2006

[3] C Guillemot and O Le Meur ldquoImage inpainting overview andrecent advancesrdquo IEEE Signal Processing Magazine vol 31 pp127ndash144 2014

[4] M Bertalmio G Sapiro V Caselles and C Ballester ldquoImageinpaintingrdquo in Proceedings of the 27th annual conference onComputer graphics and interactive techniques (SIGGRAPH rsquo00)pp 417ndash424 July 2000

[5] A BugeauM Bertalmio V Caselles and G Sapiro ldquoA compre-hensive framework for image inpaintingrdquo IEEE Transactions onImage Processing vol 19 no 10 pp 2634ndash2645 2010

[6] T F Chan and J Shen ldquoNontexture inpainting by curvature-driven diffusionsrdquo Journal of Visual Communication and ImageRepresentation vol 12 no 4 pp 436ndash449 2001

[7] D Tschumperle and R Deriche ldquoVector-valued image regular-ization with PDEs a common framework for different appli-cationsrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 27 no 4 pp 506ndash517 2005

[8] J Sun L Yuan J Jia and H Y Shum ldquoImage completion withstructure propagationrdquo ACM Transactions on Graphics vol 24pp 861ndash868 2005

[9] M M Oliviera B Bowen R McKenna and Y S Chang ldquoFastdigital image inpaintingrdquo in Proceedings of the InternationalConference on Visualization Imaging and Image Processing(VIIP rsquo01) pp 261ndash266 2001

[10] A Telea ldquoAn image inpainting technique based on the fastmarching methodrdquo Journal of Graphics Tools vol 9 pp 23ndash342004

[11] B Yan Y Gao K Sun and B Yang ldquoEfficient seam carvingfor object removalrdquo in Proceedings of the 20th IEEE Interna-tional Conference on Image Processing (ICIP rsquo13) pp 1331ndash1335September 2013

[12] A A Efros and T K Leung ldquoTexture synthesis by non-parametric samplingrdquo in Proceedings of the 7th IEEE Interna-tional Conference onComputer Vision (ICCVrsquo99) pp 1033ndash1038Corfu Greece September 1999

[13] A A Efros and W T Freeman ldquoImage quilting for texturesynthesis and transferrdquo in Proceedings of the 28th AnnualConference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo01) pp 341ndash346 Los Angeles Calif USA August2001

[14] D J Heeger and J R Bergen ldquoPyramid-based texture anal-ysissynthesisrdquo in Proceedings of the 22nd Annual ACM Con-ference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo95) vol 29 pp 229ndash238 Los Angeles Calif USAAugust 1995

[15] J S de Bonet ldquoMultiresolution sampling procedure for analysisand synthesis of texture imagesrdquo in Proceedings of the 24thAnnual Conference on Computer Graphics and Interactive Tech-niques (SIGGRAPH rsquo97) pp 361ndash368 Los Angeles Calif USAAugust 1997

[16] H Igehy and L Pereira ldquoImage replacement through texturesynthesisrdquo in Proceedings of the International Conference onImage Processing vol 3 pp 186ndash189 Santa Barbara Calif USAOctober 1997

[17] A Criminisi P Perez and K Toyama ldquoRegion filling andobject removal by exemplar-based image inpaintingrdquo IEEETransactions on Image Processing vol 13 no 9 pp 1200ndash12122004

[18] I Drori D Cohen-Or and H Yeshurun ldquoFragmentmdashbasedimage completionrdquo ACM Transactions on Graphics vol 22 pp303ndash312 2003

[19] C Guillemot M Turkan O L Meur and M Ebdelli ldquoImageinpainting using LLE-LDNR and linear subspace mappingsrdquo inProceedings of the IEEE International Conference on AcousticsSpeech and Signal Processing (ICASSP rsquo13) pp 1558ndash1562 May2013

[20] J Hays and A Efros ldquoScene completion using millions ofphotographsrdquo ACM Transactions on Graphics (SIGGRAPH2007) vol 26 no 3 2007

The Scientific World Journal 11

[21] O LeMeur and C Guillemot ldquoSuper-resolution-based inpaint-ingrdquo in Proceedings of European Conference on Computer Vision(ECCV rsquo12) pp 554ndash567 2012

[22] Z Xu and J Sun ldquoImage inpainting by patch propagation usingpatch sparsityrdquo IEEE Transactions on Image Processing vol 19no 5 pp 1153ndash1165 2010

[23] J Aujol S Ladjal and S Masnou ldquoExemplar-based inpaintingfrom a variational point of viewrdquo SIAM Journal onMathematicalAnalysis vol 42 no 3 pp 1246ndash1285 2010

[24] M Bertalmio L Vese G Sapiro and S Osher ldquoSimultaneousstructure and texture image inpaintingrdquo IEEE Transactions onImage Processing vol 12 no 8 pp 882ndash889 2003

[25] L Atzori and F G B de Natale ldquoError concealment in videotransmission over packet networks by a sketch-based approachrdquoSignal Processing ImageCommunication vol 15 no 1 pp 57ndash761999

[26] A Rares M J T Reinders and J Biemond ldquoEdge-based imagerestorationrdquo IEEE Transactions on Image Processing vol 14 no10 pp 1454ndash1468 2005

[27] M M Hadhoud K A Moustafa and S Z Shenoda ldquoDigitalimages inpainting using modified convolution based methodrdquoinOptical Pattern Recognition XX vol 7340 of Proceedings of theSPIE Orlando Fla USA April 2009

[28] P Perona and J Malik ldquoScale-space and edge detection usinganisotropic diffusionrdquo IEEE Transactions on Pattern Analysisand Machine Intelligence vol 12 no 7 pp 629ndash639 1990

[29] M Daisy D Tschumperle and O Lezoray ldquoA fast spatialpatch blending algorithm for artefact reduction in pattern-based image inpaintingrdquo in SIGGRAPH Asia 2013 TechnicalBriefs (SA rsquo13) pp 1ndash4 article 8 ACM New York NY USA2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

10 The Scientific World Journal

restoration methods and to emphasize the advantages anddisadvantages for each of the approached algorithms

Due to the fact that a certain number of inpaintingmethods have been proposed during the last years it is stilldifficult to designate the appropriate one The algorithmschosen for our evaluation are representative for the categoriesthey belong to having as reference the first one developedby Bertalmio Other methods were also analyzed as theone proposed by Oliveira and its adapted version proposedby Hadhoud et al suitable for images without texturesRegarding these two methods an alternative to the diffusionbarriers was proposed by us The restoration of texturedimages had also been taken into account in our evaluationby using the method developed by Efros and Leung and thealgorithm proposed by Criminisi

It was also important to determine the algorithm param-eters that lead to the best PSNR results and selectingrepresentative test images to provide relevant informationThe images were restored gradually varying the width ofthe occluded area in order to analyze the influence of thisparameter The tests have shown that inpainting algorithmsinvolving diffusion operations perform well for structuralfeatures images but cannot successfully rebuild textures

Image restoration using the RGB color system for thealgorithm developed by Bertalmio led to successful resultsfor structural images The adaptation proposed for Oliveirarsquosand Hadhoudrsquos algorithms has been proven to be a successfulalternative for edge preserving with remarkable resultsHowever textural inpainting techniques are themost success-ful Even if requiring a longer processing time they performwell on both image types

Further developments of this work may consist ofimplementing hybrid methods that combine features of theapproached algorithms and comparing their results with theones belonging to the already analyzed methods Hybridmethods would require reconstruction processes for thecontour lines and restoration processes over the obtainedregions by means of textural inpainting techniques

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

References

[1] P Patel A Prajapati and SMishra ldquoReviewof different inpaint-ing algorithmsrdquo International Journal of Computer Applicationsvol 59 no 18 pp 30ndash34 2012

[2] M E Taschler ldquoA comparative analysis of image inpaintingrdquoTech Rep University of York York UK 2006

[3] C Guillemot and O Le Meur ldquoImage inpainting overview andrecent advancesrdquo IEEE Signal Processing Magazine vol 31 pp127ndash144 2014

[4] M Bertalmio G Sapiro V Caselles and C Ballester ldquoImageinpaintingrdquo in Proceedings of the 27th annual conference onComputer graphics and interactive techniques (SIGGRAPH rsquo00)pp 417ndash424 July 2000

[5] A BugeauM Bertalmio V Caselles and G Sapiro ldquoA compre-hensive framework for image inpaintingrdquo IEEE Transactions onImage Processing vol 19 no 10 pp 2634ndash2645 2010

[6] T F Chan and J Shen ldquoNontexture inpainting by curvature-driven diffusionsrdquo Journal of Visual Communication and ImageRepresentation vol 12 no 4 pp 436ndash449 2001

[7] D Tschumperle and R Deriche ldquoVector-valued image regular-ization with PDEs a common framework for different appli-cationsrdquo IEEE Transactions on Pattern Analysis and MachineIntelligence vol 27 no 4 pp 506ndash517 2005

[8] J Sun L Yuan J Jia and H Y Shum ldquoImage completion withstructure propagationrdquo ACM Transactions on Graphics vol 24pp 861ndash868 2005

[9] M M Oliviera B Bowen R McKenna and Y S Chang ldquoFastdigital image inpaintingrdquo in Proceedings of the InternationalConference on Visualization Imaging and Image Processing(VIIP rsquo01) pp 261ndash266 2001

[10] A Telea ldquoAn image inpainting technique based on the fastmarching methodrdquo Journal of Graphics Tools vol 9 pp 23ndash342004

[11] B Yan Y Gao K Sun and B Yang ldquoEfficient seam carvingfor object removalrdquo in Proceedings of the 20th IEEE Interna-tional Conference on Image Processing (ICIP rsquo13) pp 1331ndash1335September 2013

[12] A A Efros and T K Leung ldquoTexture synthesis by non-parametric samplingrdquo in Proceedings of the 7th IEEE Interna-tional Conference onComputer Vision (ICCVrsquo99) pp 1033ndash1038Corfu Greece September 1999

[13] A A Efros and W T Freeman ldquoImage quilting for texturesynthesis and transferrdquo in Proceedings of the 28th AnnualConference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo01) pp 341ndash346 Los Angeles Calif USA August2001

[14] D J Heeger and J R Bergen ldquoPyramid-based texture anal-ysissynthesisrdquo in Proceedings of the 22nd Annual ACM Con-ference on Computer Graphics and Interactive Techniques (SIGGRAPH rsquo95) vol 29 pp 229ndash238 Los Angeles Calif USAAugust 1995

[15] J S de Bonet ldquoMultiresolution sampling procedure for analysisand synthesis of texture imagesrdquo in Proceedings of the 24thAnnual Conference on Computer Graphics and Interactive Tech-niques (SIGGRAPH rsquo97) pp 361ndash368 Los Angeles Calif USAAugust 1997

[16] H Igehy and L Pereira ldquoImage replacement through texturesynthesisrdquo in Proceedings of the International Conference onImage Processing vol 3 pp 186ndash189 Santa Barbara Calif USAOctober 1997

[17] A Criminisi P Perez and K Toyama ldquoRegion filling andobject removal by exemplar-based image inpaintingrdquo IEEETransactions on Image Processing vol 13 no 9 pp 1200ndash12122004

[18] I Drori D Cohen-Or and H Yeshurun ldquoFragmentmdashbasedimage completionrdquo ACM Transactions on Graphics vol 22 pp303ndash312 2003

[19] C Guillemot M Turkan O L Meur and M Ebdelli ldquoImageinpainting using LLE-LDNR and linear subspace mappingsrdquo inProceedings of the IEEE International Conference on AcousticsSpeech and Signal Processing (ICASSP rsquo13) pp 1558ndash1562 May2013

[20] J Hays and A Efros ldquoScene completion using millions ofphotographsrdquo ACM Transactions on Graphics (SIGGRAPH2007) vol 26 no 3 2007

The Scientific World Journal 11

[21] O LeMeur and C Guillemot ldquoSuper-resolution-based inpaint-ingrdquo in Proceedings of European Conference on Computer Vision(ECCV rsquo12) pp 554ndash567 2012

[22] Z Xu and J Sun ldquoImage inpainting by patch propagation usingpatch sparsityrdquo IEEE Transactions on Image Processing vol 19no 5 pp 1153ndash1165 2010

[23] J Aujol S Ladjal and S Masnou ldquoExemplar-based inpaintingfrom a variational point of viewrdquo SIAM Journal onMathematicalAnalysis vol 42 no 3 pp 1246ndash1285 2010

[24] M Bertalmio L Vese G Sapiro and S Osher ldquoSimultaneousstructure and texture image inpaintingrdquo IEEE Transactions onImage Processing vol 12 no 8 pp 882ndash889 2003

[25] L Atzori and F G B de Natale ldquoError concealment in videotransmission over packet networks by a sketch-based approachrdquoSignal Processing ImageCommunication vol 15 no 1 pp 57ndash761999

[26] A Rares M J T Reinders and J Biemond ldquoEdge-based imagerestorationrdquo IEEE Transactions on Image Processing vol 14 no10 pp 1454ndash1468 2005

[27] M M Hadhoud K A Moustafa and S Z Shenoda ldquoDigitalimages inpainting using modified convolution based methodrdquoinOptical Pattern Recognition XX vol 7340 of Proceedings of theSPIE Orlando Fla USA April 2009

[28] P Perona and J Malik ldquoScale-space and edge detection usinganisotropic diffusionrdquo IEEE Transactions on Pattern Analysisand Machine Intelligence vol 12 no 7 pp 629ndash639 1990

[29] M Daisy D Tschumperle and O Lezoray ldquoA fast spatialpatch blending algorithm for artefact reduction in pattern-based image inpaintingrdquo in SIGGRAPH Asia 2013 TechnicalBriefs (SA rsquo13) pp 1ndash4 article 8 ACM New York NY USA2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

The Scientific World Journal 11

[21] O LeMeur and C Guillemot ldquoSuper-resolution-based inpaint-ingrdquo in Proceedings of European Conference on Computer Vision(ECCV rsquo12) pp 554ndash567 2012

[22] Z Xu and J Sun ldquoImage inpainting by patch propagation usingpatch sparsityrdquo IEEE Transactions on Image Processing vol 19no 5 pp 1153ndash1165 2010

[23] J Aujol S Ladjal and S Masnou ldquoExemplar-based inpaintingfrom a variational point of viewrdquo SIAM Journal onMathematicalAnalysis vol 42 no 3 pp 1246ndash1285 2010

[24] M Bertalmio L Vese G Sapiro and S Osher ldquoSimultaneousstructure and texture image inpaintingrdquo IEEE Transactions onImage Processing vol 12 no 8 pp 882ndash889 2003

[25] L Atzori and F G B de Natale ldquoError concealment in videotransmission over packet networks by a sketch-based approachrdquoSignal Processing ImageCommunication vol 15 no 1 pp 57ndash761999

[26] A Rares M J T Reinders and J Biemond ldquoEdge-based imagerestorationrdquo IEEE Transactions on Image Processing vol 14 no10 pp 1454ndash1468 2005

[27] M M Hadhoud K A Moustafa and S Z Shenoda ldquoDigitalimages inpainting using modified convolution based methodrdquoinOptical Pattern Recognition XX vol 7340 of Proceedings of theSPIE Orlando Fla USA April 2009

[28] P Perona and J Malik ldquoScale-space and edge detection usinganisotropic diffusionrdquo IEEE Transactions on Pattern Analysisand Machine Intelligence vol 12 no 7 pp 629ndash639 1990

[29] M Daisy D Tschumperle and O Lezoray ldquoA fast spatialpatch blending algorithm for artefact reduction in pattern-based image inpaintingrdquo in SIGGRAPH Asia 2013 TechnicalBriefs (SA rsquo13) pp 1ndash4 article 8 ACM New York NY USA2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of