digital inpainting

7/31/2019 Digital Inpainting

1/18

11

Seminar on

Digital Image Inpainting

ByMs. Vidhya M. Shinde

Under the Valuable Guidance of

Prof. V. U. Deshmukh


2/18

22

Literature Survey


3/18

33

IntroductionInpainting:-

A set of techniques for making undetectablemodifications to images

Reconstructing damaged old photographs andremoving unwanted objects from images/videos andimproving overall composition

Automatic Digital Inpainting is a technique which

restores damaged image or video by means of imageinterpolation.

Inpainting is the art of restoring lost/selected parts ofan image based on the background information in a

visually plausible way.


4/18

4

Algorithms:-

1. Exemplar Based Image Inpainting

2. Successive Elimination Algorithm

3. Multilayer or multi-resolution ImageInpainting Algorithm


5/18

55

Exemplar Based Image Inpainting

Figure: Notation Diagram

Computing patch priorities

Texture Synthesis

Filling order


6/18

1. Computing patch priorities:-

priority P(p) as the product of two terms:

P(p) = C(p)D(p) .. (1)

C(p) the confidence term

(2)D(p) the data term,

.(3)6


7/18

The patch with highest priority is to be computed

Propagate image texture by direct sampling of the

source region

Search in the source region for that patch which ismost similar to p

7

2. Texture Synthesis:


8/18

3. Filling order:-

Match the patch window with entire image from(0,0)thpixel to (x,y)th pixel and identify the matched window

Collect all the matched window position and again findC(p) and D(p) to compute current P(p)

Find highest priority matched window then replace theprevious patch window with currently got matchedwindow

8


9/18

9

Successive Elimination Algorithm

To increased the computational efficiency

Based on exemplar- based image inpaintingalgorithm

It uses Sum of Absolute Difference (SAD) to obtainglobal optimal solution

It can be used for Restoration of Small scratches andalso for reconstruction of image after removal oflarge object from the image


10/18

10

Steps of SEA algorithm:

1. Sum of Absolute Difference (SAD):

2. Choose the most prior patch

3. Initialize MinSAD :-

4. Compute the Sum of MinSAD


11/18

11

Cont..

5. {Compute the sum of the current patch

6. if ( R-Min SAD M(m,n) R+MinSAD)

{ compute the SAD value of the current patch;

if ( SAD MinSAD){ MinSAD=SAD; }

} }

7. Find out the position of the patch that the minimum

value of SAD corresponds to, and this patch q isthe most matching.

8. Copy the information in q p p;

9. Update the value of C(p) p p;

}


12/18

1212

Disadvantages

Detects only objects with sharp edges Smoothing may miss edges in presence of noise

these algorithms is that the diffusion process

introduces some blur e.g.


13/18

1313

Applications

1. In Photography:- Restoration of photographs, films and valuable

paintings

Removing Red-Eye

Zooming and to produce special effects

2. Removal of Occlusion:-

Removing stamped date, text subtitle, stamp,publicity from images

Removing objects to creative effects

Removing logos from videos

3. Wireless Image Transmission:-

To replace lost blocks in coding and transmission ofimages e.g. in Streaming Video


14/18

1414

Examples

1. Removal of Large object from image:-Original image

50 100 150 200

50

100

150

200

250

300

Fill region

50 100 150 200

50

100

150

200

250

300

Inpainted image

50 100 150 200

50

100

150

200

250

300


15/18

1515

Examples

2. Reconstruct Noisy image:-Original Nature Corrupted - 50% Inpainted Nature


16/18

1616

Conclusion


17/18

1717

References


18/18

1818

digital inpainting

Documents