Local median information based adaptive fuzzy filter for ... · Fig.4-(a) Noisy cameraman image,(b)Output of CWM filter,(c)Output of median Filter,(d)Output of MDB filter,(e)Output

International Journal of Advanced Computer Engineering and Communication Technology (IJACECT)

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ISSN (Print): 2278-5140, Volume-2, Issue – 3, 2013

27

Local median information based adaptive fuzzy filter for impulse

noise removal

1Prajnaparamita Behera,

2Shreetam Behera

1Final Year Student, M.Tech VLSI Design, Dept. of ECE,

2Asst .Professor, Dept. ECE

CIT, Centurion University of Technology & Management Jatni (Odisha), India

Email: [email protected]

Abstract— Impulse noise removal is still a great

challenging job in the field of image processing. Lots

of linear and nonlinear filters have been proposed

earlier for the impulse noise removal but it is found

that they degrade the quality of images by blurring.

In this paper a two pass median filter is used to

remove impulse noise. In the first pass min-max

based median filter is used for detection and

correction of noisy pixel. In the second pass local

median information based adaptive fuzzy filter is

used to denoise the image. The proposed method is

efficient, fast and results in a higher PSNR (Peak

Signal to Noise Ratio) values when compared to

other traditional filters.

Keywords: Impulse noise, blurring, Min-max based

median filter, Adaptive, PSNR

I. INTRODUCTION

Image denoising is the most important and challenging

job in the field of image processing. During the time of

data acquiring, broadcasting and loading the image

becomes partial. The noise is come into the images when

captured by camera or scanner or while recording and

when the image is transmitted by a noisy channel. Salt

and pepper noise is one type of noise which is impulsive

in nature and most of the techniques used for its removal

has nonlinear characteristics. Median filter is the most

popular nonlinear filter in image processing .The median

filter is not appropriate for non-impulsive noise

reduction. The Weighted Median (WM) filter is the

modification of standard median filter where a specific

weight is given to every pixel present in the window.

CWM is a special type of weighted median filter where

weight is specified only the centre pixel of the window.

The standard median filter is the most popular nonlinear

filter for noise reduction. But in case of large window

and high noise it gives rise to more blurring as

comparison to CWM. To avoid this obscuring of images

a MDB filter was introduced in [1]. This proposed

technique was found to be more superior than the centre

weighted median filter.

In [2] the authors introduced an algorithm in which the

noisy pixel is replaced by trimmed median value for

denoising the images and it is found to be better in

comparison with the standard median filter.

To produce more effective and reduced noise levels ,

median filter is imbibed with fuzzy technique by the

authors in [3] .A switching based fuzzy scheme is

introduced by the authors in [4] which is able to

eliminate impulse noise from grayscale images to a

greater extent. It was also seen that with the increase in

the processing window more accurate result was obtained

in [5].In [6], the authors proposed a novel approach to

detect and remove impulse noise with an additional aim

of enhancing the image. The efficiency of adaptive fuzzy

filter is well demonstrated in [7] with respect to other

traditional median filters.

In this paper a two pass median filtering scheme is

proposed for removal of impulse noise from heavily

corrupted images. The proposed technique is explained

in the section II. Section III analyses and explains the

results of the proposed fuzzy scheme followed by the

conclusion and references..

II. PROPOSED SCHEME FOR IMAGE

DENOISING

In this paper a two pass median filtering scheme is

proposed, where in the first pass, the noise is detected

and corrected using a Min-Max Based detection based

median filter and then an adaptive fuzzy filter based on

local window information is used in the second pass. The

flow charts give a brief outline of the proposed method.

In the first pass the noise detected when the pixel value

is greater than the maximum of the window pixels or less

than the minimum of the window pixels and it is replaced


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by the median of the window pixels else left unchanged.

In the second pass to this data obtained from the first

pass, local information is found out based on the median

values of the absolute gradient values.

Fig.1:Flow chart for Proposed Filtering scheme

Fig.2: Flow chart for Fuzzy Filtering scheme

This local information data is fuzzified using on basis of

the fuzzy rules given below:

1. If L(i,j) is large ,then µ(L(i,j)) is large.

2. If L (i,j) is small ,then µ(L(i,j)) is small.

It was found that S shaped membership function given

below satisfied the above rules and thus was used for the

fuzzification of the local information data.

Where a & b are any fixed thresholds.

Then the corrected pixel is obtained by

Where C(i,j)=Corrected image

Y(i,j)=Input image

O(i,j)=Image obtained after the first pass

filtering

µ(L(i,j))=Fuzzified Local information data

III.SIMULATION RESULTS

The performance of the new scheme has been executed

and compared with the existing traditional filters. In our

implementation standard grayscale images of size 256

x256 of Cameraman and Lena are degraded by salt and

pepper noise at various densities (10% to 80%) and

restored by using various methods. Peak Signal to Noise

ratio (PSNR) is used as an evaluation tool for comparing

different denoising schemes. Peak signal to noise ratio

for a gray scale image is defined as:

Where X (i,j) is the original pixel and Y(i,j) is the

restored pixel.

Figure 3 carry out the original image of Cameraman of

size 256 x256 and Lena which are restored afterwards

using the proposed technique and exposed in figure 4 and

5 accordingly. The estimated value of PSNR is tabulated

in Table 1 for Cameraman and Table 2 for Lena from

which we can easily distinguish the prominence of the

proposed method. The comparisons of different median

filtering schemes with the offered technique are shown in

the graphs in Figure 6 and Figure 7.

(a) (b)

Fig.3:(a)Original image of Cameraman,(b)Original

Image of Lena.


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Output

obtained

by using

different

filters

Noise Levels

10% 20% 30% 40% 50% 60% 70% 80%

CWM 34.0704 27.5984 23.5606 20.6747 18.3456 16.3838 14.7059 13.1256

Median 34.3729 27.7820 23.7816 20.9856 18.6689 16.7341 14.9706 13.2653

MDB 34.4074 27.8356 23.8518 21.0749 18.7612 16.8068 15.0665 13.3689

Fuzzy 34.4838 27.8702 23.8716 21.0876 18.7697 16.8127 15.0703 13.3712

Table 1:Comparison Table for PSNR values of Cameraman at various technique with different noise densities .

(a)

(b)

(c)

(d)

(e)

Cameraman image Corrupted by 10% noise

(a)

(b)

(c)

(d)

(e)


(a)

(b)

(c)

(d)

(e)


(a)

(b)

(c)

(d)

(e)


Fig.4-(a) Noisy cameraman image,(b)Output of CWM filter,(c)Output of median Filter,(d)Output of MDB

filter,(e)Output of Proposed fuzzy based filter.


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30

(a)

(b)

(c)

(d)

(e)

Lena image Corrupted by 10% noise

(a)

(b)

(c)

(d)

(e)


(a)

(b)

(c)

(d)

(e)


(a)

(b)

(c)

(d)

(e)


Fig.5-(a) Noisy Lena image,(b)Output of CWM filter,(c)Output of median Filter,(d)Output of MDB

filter,(e)Output of Proposed fuzzy based filter.

Output

obtained

by using

different

filters

Noise Levels

10% 20% 30% 40% 50% 60% 70% 80%

CWM 35.5064 28.6780 2.4252 21.4766 18.9458 16.9171 15.1398 13.3418

Median 35.7050 28.9014 24.7793 21.9496 19.5421 17.4858 15.6039 13.6232

MDB 35.7499 28.9483 24.8486 22.0331 19.6121 17.5784 15.6985 13.7142

Fuzzy 35.8172 28.9759 24.6821 22.0393 19.6146 17.5793 15.6987 13.7142 Table 2:Comparison Table for PSNR values of Lena at various technique with different noise densities .


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10 20 30 40 50 60 70 8010

15

20

25

30

35

noise

psnr

comparision of %salt and pepper noise vs. PSNR

psnr=cwm

psnr=median

psnr=mdb

psnr=fuzzy

Fig.6: Comparison of PSNR vs. salt and pepper noise of

Cameraman at various noise densities.

10 20 30 40 50 60 70 8010

15

20

25

30

35

40

noise

psnr

comparision of %salt and pepper noise vs. PSNR

psnr=cwm

psnr=median

psnr=mdb

psnr=fuzzy

Fig.7: Comparison of PSNR vs. salt and pepper noise of

Lena at various noise densities.

IV .CONCLUSION

Filtering effect becomes appreciable with higher PSNR

values. For the images the subjective analysis of the

image depicts the quality of the image. In this research

work, the PSNR values for the proposed filtering

scheme was found to be higher than the other traditional

methods and it was also found the images have better

quality when analyzed subjectively with respect to other

denoising methods.

V. REFERENCES

[1] S. K. Satpathy, S. Panda, K. K. Nagwanshi and

C. Ardil “Image Restoration in Non-Linear

Filtering Domain using MDB approach”

,International Journal of Information and

Communication Engineering Volume 6,Issue 1

2010,pp45-49.

[2] Aswini Kumar Samantaray and Priyadarshi

Kanungo “First order neighborhood decision

based median filter” 2012 World Congress on

Information and Communication Technologies

Vol.6, pp 785-789.

[3] Bhavana Deshpande, H.K. Verma & Prachi

Deshpande “Fuzzy based median filtering for

removal of salt & pepper noise” International

journal of soft computing &

Engineering,IJSCE,ISSN:2231-2307,Volume-

2,Issue-3,pp 76-80,July 2012.

[4] R.Pushpavalli & G.Sivarajde, “A Fuzzy

Switching Median Filter for Highly Corrupted

Images” International journal of Science and

Research Publications ,ISSN 2250-3156,Volume-

3,Issue-6.pp.1-6,June 2013.

[5] Isavani Perumal.P&Murugappriya.S,

“Implementation of Cluster based Adaptive

Fuzzy Switching Median Filter for Impulse Noise

Removal”, IJMER, ISSN: 2249-6645, Volume-

2.Issue-3, pp 1306-1309,May-June 2012.

[6] M.Suneel, K.Samba Siva Rao, M.Lavanya &

M.Sai Sasanka “Fuzzy Enhancement Technique

using S-membership Function in Medical

applications”, IJECE, ISSN: 2278-9901, Volume-

2, Issue-2, pp.121-126, May 2013.

[7] CharuKhare and Kapil Kumar Nagwanshi

“Image Restoration Technique with Non Linear

Filter” International Journal of Advanced

Science and Technology Vol. 39, February.

[8] Shanmugavadivu P1 and Eliahim Jeevaraj P S2 “

Adaptive PDE based median filter for the

restoration of high density impulse noise

corrupted images” International Journal of

Advanced Information Technology (IJAIT) Vol.

1, No.6, December 2011,pp 43-51.

[9] J.Sorubamarcel, A.Jayachandran, G.Kharmega &

Sundararaj, “An efficient algorithm for removal

of impulse noise using adaptive fuzzy switching

weighted median filter”, IJCTEE,ISSN 2249-

6343,Volume-2,Issue-2,pp 1-8,2012.

[10] Roli Bansal, Priti Sehgal & Punam Bedi, “A

Simplified Fuzzy Filter for Impulse Noise

Removal using Thresholding”,WCECS,2007

October pp24-26, San Francisco, USA.

[11] Rafael C. Gonzalez& Richard E. Woods, “Digital

Image Processing”, Pearson,3rd Edition,2009.

[12] S. N. Sivanandam& S. N. Deepa, “Principles of

Soft Computing”, Wiley India, Second Edition,

2011.

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Local median information based adaptive fuzzy filter for ... · Fig.4-(a) Noisy cameraman image,(b)Output of CWM filter,(c)Output of median Filter,(d)Output of MDB filter,(e)Output