an efficient component based filter for random valued impulse

International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 3 (2016) pp 1908-1915

© Research India Publications. http://www.ripublication.com

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An Efficient Component Based Filter for Random Valued Impulse Noise

Removal

Manohar Koli

Research Scholar, Department of Computer Science, Tumkur University, Tumkur, Karnataka, India.

S. Balaji

Centre for Emerging Technologies, Jain Global Campus, Jain University, Jakkasandra Post, Kanakapura Taluk, Ramanagara Dist., Karnataka, India.

Abstract

In this digital world, due to faulty sensors, storage, and

channels images videos are often corrupted by impulse noise,

which is a frequently occurring noise type in an image.

Impulse noise are classified into fixed valued (salt and pepper

noise) and random valued impulse noise. This paper proposes

an effective noise reduction method for images corrupted by

the random valued impulse noise, handling of which is more difficult than the salt and pepper impulse noise. Our method is

based on the concept that the impulse noise produces small

components (patches) on an image less than 10x10 size. We

convert a gray scale image into a binary image and analyze

the components less than 10x10 size on various parameters

and remove them using neighboring pixel connectivity.

Secondly, removed pixels are filled by the estimated value of

median filter calculated with the help of neighboring pixels in

that area. Comparison of the proposed algorithm with other

existing algorithms shows that the proposed component based

filter performs better than all other existing algorithms. The

visual and quantitative results show that the performance of the algorithm is very good and it handles more than 70%

noise.

Keywords: Filtering, Component Based Filter, Random

Valued Impulse Noise, Image Restoration.

Introduction The impulse noise is one which may corrupt the images during their acquisition, transmission or storage. Several

algorithms have been proposed to remove the impulse noise in

the images. Random Valued Impulse Noise (RVIN) assumes a

noise value between the minimum value 0 and the maximum

value 255 of the noise, as shown in equation (1) [1-2].

(1)

The median and the mean value based filters are the most

popular non-linear filters. When an image contains a small

amount of noise, they are efficient but they do not handle the

large percentage of noise. Hence, in this paper, a non-linear component based filter is proposed. In literature, it is observed

that only few algorithms are proposed to handle RVIN. Our

main aim is to provide a better solution to RVIN than the

available algorithms in the literature. The proposed

Component Based Filter (CBF) is compared with Adaptive

Median Filter (AMF) [3], Progressive Switching Median

Filter (PSMF) [4], Tri-State Median Filter (TSMF) [5],

Adaptive Fuzzy Switching Filter (AFSF) [6], A New Impulse

Detector Based on Order Statistics Filter (NIND) [7], An

Efficient Algorithm for the Removal of Impulse Noise from Corrupted Images (AEAFRIN) [8], A New Fast and Efficient

Decision-Based Algorithm (DBA) [9], An Improved Adaptive

Median Filter (IAMF) [10], Robust Statistics Based

Algorithm (RSBA) [11], Decision Based Adaptive Median

Filter (DBAF) [12], Image Restoration in Non-linear Filtering

Domain Using MDB Approach (MDBF) [13], Detail

Preserving Adaptive Filter (DPAF) [14] and A Universal De-

noising Framework (UDF) [15].

Proposed CBF Algorithm 1. Take input gray scale image (X).

2. Convert gray scale input image (X) to a binary image

(Y).

3. Identify the connected components using 8

neighboring pixel connectivity. Remove connected

components having less than 10x10 size and having

pixels less than 20. In 10x10 (100 pixels) sized

components, components can have maximum 100

pixels in it. Since usually noisy components contain

sparsely distributed pixels, they can have very less number of pixels in 10x10 area. Hence, If component

is made up of less than or equal to 20 pixels then we

consider that component is noisy component else we

consider component is non-noisy component and we

will recover the noisy components.

4. Replace all noisy-component pixels by the median

value calculated using the neighboring non-corrupted

pixels to the image.

5. Calculate the restored image R (x, y) using 3x3 median

filter. Using R and I, compute the difference image D

(x, y) and convert D to a binary noise image (Bn). If Bn (x, y) == 1 restore pixels using their neighboring

non-corrupted pixels and Set I = R.

6. Using the Centre weighted median filter restore I

recursively.



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Step by step outputs from the CBI algorithm for IMAGE-1 is

shown in Figure 1.

Step 1: Input Gray Image IMAGE-1 with 20% RVIN (X) Step 2: Binary Image (Y)

Step 3: Noisy connected component having size less than 10X10

and less than 20 connected pixels. Step 4: Image after recovering all noisy components using

neighboring non noisy pixels.

Step 5: Restored image using 3X3 median filter. Step 6: Restored image using center weighted median filter

recursively.

Figure 1: Restoration Results of Image-1 with 20% RVIN

Performance Measurements To evaluate the performance of the proposed algorithm, four

different natural images (IMAGE-2, IMAGE-3, IMAGE-4,

and IMAGE-5) are used. The performance is measured using

Error Recovery Percentage (ERP) as shown in equations (2)

and (3). Figure 2 and Figure 3 show restoration results of our

algorithm for the images IMAGE-2 and IMAGE-3 for

different amounts of noise. Visibility of output of 70% noisy

image clearly shows that the efficiency of our algorithm is

very high. Figure 4 and Figure 5 show restoration results of different filters. The visibility of the outputs of our algorithm

clearly shows that efficiency of our algorithm is high

compared to other algorithms. Calculated ERP for image IMAGE-4 and IMAGE-5 are shown in Table 1 and Table 2.

Compared to other popular algorithms ERP value of our

algorithm is very high. The results are shown graphically in

Figure 6 and Figure 7.

(2)



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(3) Where

X - Original Image

R - Restored Image

MXN - Size of Image

MAE - Mean Absolute Error

ERP - Error Recovery Percentage.

Table 1: ERP Values of Filters for RVIN IMAGE-4

(300X300)

NOISE ► 10 20 30 40 50 60 70 80 90

FILTERS ▼

AMF 57.71 50.55 42.19 35.01 28.18 21.6 17.17 13.68 11.38

PSMF 84.84 86.15 86.21 84.49 78.55 66.45 50.08 35.96 25.44

TSMF 9.61 53.49 66.61 68.97 63.03 52.89 41.04 32.18 25.79

AFSF 60.77 59.11 53.05 45.06 38.56 33.91 28.21 23.99 21.04

NIND 88.09 90.69 91.62 90.88 87.77 79.71 61.87 42.51 22.43

AEAFRIN 77.49 81.69 78.49 70.95 60.49 48.42 36.7 28.38 21.55

DBA 0.68 0.76 0.88 0.78 0.78 0.55 0.64 0.5 0.44

IAMF 85.28 85.66 84.61 82.09 65.36 40.59 20.54 8.08 2.63

RSBA 59.63 51.12 43.34 35.68 28.27 22.22 17.72 13.83 10.79

DBAF 83.8 85.04 81.23 73.13 62.07 50.18 38.78 29.18 21.84

MDBF 72.92 62.18 50.47 40.49 32.12 24.32 18.35 14.06 11.33

DPAF 65.12 56.26 47.96 39.15 29.8 22.55 17.33 13.98 11.11

UDF 73.78 81.48 83.15 82.16 74.32 58.13 41.23 30.03 21.84

PACBF 98.56 97.81 96.74 95.27 92.49 84.74 69.82 53.68 36.55

Table 2: ERP Values of Filters for RVIN IMAGE-5

(300X300)

NOISE ► 10 20 30 40 50 60 70 80 90

FILTERS ▼

AMF 79.41 69.25 59.41 50.73 43.01 36.1 29.09 23.24 18.43

PSMF 90.82 90.86 90.51 89.45 86.85 80.55 70.44 55.98 40.7

TSMF 41.42 69.6 78.83 81.71 79.7 73.03 62.14 52.13 41.77

AFSF 94.24 92.58 88.48 82.08 73.57 63.35 54.12 44 34.5

NIND 96.25 96.02 95.7 94.87 93.13 87.56 75.13 55.52 34.13

AEAFRIN 91.55 90.66 88.26 82.26 74.11 64.95 53.85 44.16 34.39

DBA 1.65 1.43 1.29 1.47 1.26 1.23 1.08 1.06 0.99

IAMF 90.26 89.81 88.97 87.81 84.15 69.7 37.7 13.03 4.93

RSBA 79.96 69.25 59.54 50.26 43.06 36.18 29.25 23.64 18.56

DBAF 96.1 94.58 91.34 85.03 76.27 66.24 54.94 44.3 34.76

MDBF 80.14 69.13 59.55 51.01 42.91 35.43 29.19 23.48 18.66

DPAF 80.37 68.96 59.7 50.75 43.08 36.05 28.8 23.15 18.39

UDF 95.63 95.29 94.8 94.03 93.04 90.98 84.37 72.55 60.18

PACBF 99.06 98.31 97.14 96.73 95.14 92.72 87.12 72.93 53.59

ORIGINAL IMAGE-2 (300X300)

05% NOISE 20% NOISE

RESTORED IMAGE OF 05% NOISE

ERP= 95.25


ERP= 93.77



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35% NOISE 50% NOISE 65% NOISE


ERP= 91.06


ERP= 88.13


ERP= 79.06

Figure 2: Restoration Results of Image-2 up to 65% of Noise

ORIGINAL IMAGE-3 (300X300)

10% NOISE 25% NOISE

RESTORED IMAGE OF10% NOISE

ERP= 97.93


ERP= 96.18



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40% NOISE 55% NOISE 70% NOISE


ERP= 93.89


ERP= 90.63


ERP= 83.29

Figure 3: Restoration Results of Image-3 up to 70% of Noise

ORIGINAL IMAGE-4 (300X300) PACBF AMF

PSMF TSMF AFSF



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NIND AEAFRIN DBA

IAMF RSBA DBAF

MDBF DPAF UDF

Figure 4: Results of Filters for IMAGE-4 (300X300) with 30% RVIN

ORIGINAL IMAGE-5 (300X300) PACBF AMF



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PSMF TSMF AFSF

NIND AEAFRIN DBA

IAMF RSBA DBAF

MDBF DPAF UDF

Figure 5: Results of Filters for IMAGE-5 (300X300) with 70% RVIN



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Figure 6: ERP for the RVIN IMAGE-4 (300x300)

Figure 7: ERP for the RVIN IMAGE-5 (300X300).

Conclusion In this paper, an efficient component based algorithm to

remove the random valued impulse noise from gray scale

images is proposed. Experimental results show that the

efficiency of the proposed algorithm is very high compared to

the other popular algorithms reported in the literature. Further,

the proposed algorithm works well in both the low and the

high noise conditions up to 70%. This algorithm is a

promising solution for the RVIN reduction as it very

effectively handles noise and maintains consistency in performance.

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an efficient component based filter for random valued impulse

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