A Novel Color Image Denoising Technique Using Window...

International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)

Volume 3 Issue 8, August 2014

ISSN: 2278 – 1323 All Rights Reserved © 2014 IJARCET 2789

A Novel Color Image Denoising Technique Using

Window Based Soft Fuzzy Filter Hemant Kumar, Dharmendra Kumar Roy

Abstract - The image corrupted by different kinds of noises is a

frequently encountered problem in image acquisition and

transmission. The noise comes from noisy channel transmission

errors. The impulse noise (or salt and pepper noise) is caused by

sharp, unexpected disturbances in the image signal; its

appearance is randomly scattered white or black (or both) pixels

over the image. Gaussian noise is an idealized form of white

noise, which is caused by some random fluctuations in the signal.

Speckle noise (or more simply just speckle) can be modelled by

random values multiplied by pixel values; hence it is also called

multiplicative noise. This work presents a novel technique for

edge preserved color image denoising using window based soft

fuzzy filter based on asymmetrical triangular membership

function. However lots of techniques like median, mean and

average filters are available for gray image denoising, but most of

the time it is found that all these filters are capable to provide

good noise removal for some specific type of noise, but cant able

to preserve the edges of the images ie the output images were

greatly suffers from the blurring effect. So to address this

problem the proposed technique not only concentrates on

efficient noise removal as well as preservation of image edges. To

handle this problem fuzzy logic based soft technique is proposed,

because of imprecise and vague situations handling capability of fuzzy based techniques.

To illustrate the proposed method, experiments have

been performed on color test image like Lena and results are

compared with other popular image denoising methods. For the

comparative analysis of the proposed work a comparison

between conventional filters and proposed filter has been also

presented in the thesis on the basis of three important parameters

Mean square error (MSE), Peak signal to noise ratio (PSNR) and

Edge Preservation index (EPI). The obtained results show that

the proposed method has very good performance with desirable improvement in the PSNR and MSE of the image.

Index Terms—Color Image denoising, edge preservation, fuzzy

filters, membership function, triangular membership function,

PSNR, MSE, EPI (Edge Preserving Index).

I. INTRODUCTION

The fundamental problem of image and signal processing is to

effectively reduce noise from a digital image while keeping its features intact (e.g., edges). Three main types of noise exist:

impulse noise, multiplicative noise and additive noise. Impulse

noise is usually characterized by some portion of image pixels

that are corrupted, leaving the remaining pixels unaffected.

Examples of impulse noise are fixed valued impulse noise and

randomly valued impulse noise. The additive noise comes

when a value from a certain distribution is added to each one

image pixel, for example, a Gaussian distribution.

Multiplicative noise is usually more difficult to remove from

images than additive noise because the intensity of the noise

varies with the signal intensity (e.g., speckle noise). Fuzzy set

theory and fuzzy logic [1] offer us powerful tools to represent

and process human knowledge represented as fuzzy rules.

Fuzzy image processing [2] has three main stages:

1) Image Fuzzyfication,

2) Modification of membership values, and

3) Image Defuzzyfication.

The Fuzzyfication and Defuzzyfication steps are due to the

fact that we do not yet possess fuzzy hardware. Therefore, the

coding of image data (Fuzzyfication) and decoding of the

results (Defuzzyfication) are steps that make it possible to

process images with fuzzy techniques. The key power of fuzzy

image processing lies in the second step. After the image data

is transformed from input plane to the membership plane

(fuzzyfication), suitable fuzzy techniques modify the

membership values. It can be a fuzzy clustering, a fuzzy integration approach, a fuzzy rule-based approach, , etc.

This paper presents a novel technique for edge

preserved color image denoising using window based soft

fuzzy filter based on asymmetrical triangular membership

function. In this work, the input image is first divided into

three Red, Green and blue constituent single channels and then

a fuzzy membership-type of weighted functions is applied [2]

to each single channel image pixel-values within a moving

window, and define a fuzzy filter based on asymmetrical

membership function. This fuzzy filter attempt to incorporate

the feature of a moving average filter for filtering noise and

also preserves the edges of the image. Obtained results shows that this fuzzy filter have great success in filtering images with

random noise, impulse noise, Gaussian noise and speckle

noise.

II. DEFINITION OF FUZZY LOGIC BASED SOFT FILTERS

Let x (i, j) be the input of a 2-dimensional fuzzy filter, the

output of the fuzzy filter is defined as:




𝒚(𝒊, 𝒋) = 𝐅 𝐱 𝐢+𝐫 ,𝐣+𝐬 .𝐱(𝐢+𝐫,𝐣+𝐬)(𝐫,𝐬)∈𝐀

𝐅[𝐱 𝐢+𝐫,𝐣+𝐬 ](𝐫,𝐬)∈𝐀

… (1) Where F[x(i, j)] is the window function and A is the area of

the window. With different window functions, we now define

a novel fuzzy filter, which we shall call the fuzzy logic based soft filter with asymmetrical triangular membership function

with moving average center (FLBSFWATMF).

A. FUZZY LOGIC BASED SOFT FILTER WITH ASYMMETRICAL

MEMBERSHIP FUNCTION

The Fuzzy Logic Based Soft Filter with an asymmetrical

triangular function and the moving average value within a

window as the center value is defined as:

…

(2) The degree of asymmetry depends of the difference between

xmav(i, j) - xmin{i, j) and xmax (i, j)-xmav(i,j). xmin (i, j) and

xmav(i, j) represent, respectively, the maximum value, the

minimum value, the moving average value of x(i+r, j+s)

within the window A at discrete indexes (i, j).

III. METHODOLOGY

The idea of this proposed work is to average a pixel using

other pixel values from its neighborhood, but simultaneously

preserve edges of the image which should not be destroyed by

the filter. The complete methodology of the proposed is shown in figure (3.1) with the help of flow chart representation.

In this project we take input multichannel color

image after then we add noise like Gaussian noise or Speckle

noise. We extract three color images Red, Green, Blue and

after then obtain the value of Xmin, Xmax and Xmav for

Asymmetrical Membership function for Red, Green and Blue

images. Then we initialize the moving window size for fuzzy

filter development for Red, Green and Blue component. Then

we apply Fuzzy filter in row and column wise on noisy Red,

Green and Blue Component. Finally combine the three Red,

Green, Blue components of filtered image to generate combined filtered color image as output and Display Filtered

color image and Calculate MSE, PSNR, EPI.

Figure (3.1) Flow chart representation of proposed work.

Obtain values

of Xmin, Xmav

and Xmax for

Asymmetrical

Membership

function for

Red component

of input color

image.

Obtain values

of Xmin, Xmav

and Xmax for

Asymmetrical

Membership

function for

Green

component of

input color

image.

Obtain values

of Xmin, Xmav

and Xmax for

Asymmetrical

Membership

function for

Blue

component of

input color

image.

Initialize the

moving window

size for fuzzy

filter

development

for Red

component

Initialize the

moving window

size for fuzzy

filter

development

for Green

component

Initialize the

moving window

size for fuzzy

filter

development

for Blue

component

Apply fuzzy

filter defined by

eq. 1 in row and

column wise on

noisy Red

component

Apply fuzzy

filter defined by

eq. 1 in row and

column wise on

noisy Green

component

Apply fuzzy

filter defined by

eq. 1 in row and

column wise on

noisy Blue

component

Stop

Combine the three Red, Green & Blue Components of filtered

image to generate combined filtered color image as output

Display filtered color image and Calculate MSE, PSNR and EPI

Extract Red

components of

noisy input image

Extract Green

components of

noisy input image

Extract Blue

components of

noisy input image

Add Noise to the color input image

Read Multichannel (Color)

Input Image

Start




IV. RESULTS

This section presents complete visual and comparative

analysis of salt and pepper noise denoising of noisy images

using average filter, median filter and developed fuzzy based

filter. Figure (4.1) shows first noisy input image i.e. lena.jpg,

which is corrupted by 50% salt and pepper noise. To present a

comparative performance evaluation of developed algorithm,

obtained results will be compared with the median filter and

average filter, which are the most efficient filter for salt and

pepper noise removal. The results obtained after the denoising

process using developed algorithm and conventional filters are shown from figure (4.2) to figure (4.4). For example Figure

(4.2) shows the resultant image after denoising using average

filter, Figure (4.3) shows the resultant image after denoising

using median filter, and Figure (4.4) shows the resultant image

after denoising using developed fuzzy filter. Table (1) shows

the parameter values obtained after denoising using the three

filters.

Figure (4.1)

Figure (4.2)

Figure (4.3)

Figure (4.4)

TABLE I. Comparison of Various parameters wrt change in median,

average and fuzzy filters for input image 1

S.

No. Parameters

Average

Filter of

size

(10x10)

Median

Filter of

size

(10x10)

Developed

Fuzzy

Filter

1 MSE 99.10 32.46 21.20

2 PSNR 29.07 33.02 34.94

3 EPI 0.17 0.25 0.73

From table 1, it is evident that the developed fuzzy filter

provides least MSE and highest PSNR as compare to

conventional filters. In addition to this it is also clear from the

table, that the developed fuzzy filter provides highest amount

of edge preservation during first image denoising.

Similarly figure (4.5) shows second noisy input image ie.

pears.png, which is also corrupted by 50% salt & pepper

noise. The results obtained after the denoising process using

developed algorithm and conventional filters are shown from

figure (4.6) to figure (4.8). For example Figure (4.6) shows the

resultant image after denoising using average filter, Figure

(4.7) shows the resultant image after denoising using median

filter, and Figure (4.8) shows the resultant image after

denoising using developed fuzzy filter. Table (2) shows the

parameter values obtained after denoising of second input color image using three filters.




Figure (4.5)

Figure (4.6)

Figure (4.7)

Figure (4.8)

TABLE II. Comparison of Various parameters wrt change in median,

average and fuzzy filters for input image 2

From table 2 it is evident that the developed fuzzy filter



table that the developed fuzzy filter provides highest amount

of edge preservation during first image denoising. Similarly figure (4.9) shows third noisy input image

ie. Football.jpg, which is also corrupted by 50% salt & pepper

noise. The results obtained after the denoising process using

developed algorithm and conventional filters are shown from

figure (4.10) to figure (4.12). For example Figure (4.10) shows

the resultant image after denoising using average filter, Figure

(4.11) shows the resultant image after denoising using median

filter, and Figure (4.12) shows the resultant image after

denoising using developed fuzzy filter. Table (3) shows the

parameter values obtained after third image denoising using

three filters.

Figure (4.9)

S.

No. Parameters

Average

Filter of

size

(10x10)

Median

Filter of

size

(10x10)

Developed

Fuzzy

Filter

1 MSE 103.51 16.46 10.86

2 PSNR 29.30 35.97 37.81

3 EPI 0.22 0.25 0.69




Figure (4.10)

Figure (4.11)

Figure (4.12)

TABLE III. Comparison of Various parameters wrt change in

median, average and fuzzy filters for input image 3

S.

No. Parameters

Average

Filter of

size

(10x10)

Median

Filter of

size

(10x10)

Developed

Fuzzy

Filter

1 MSE 25.82 35.27 25.18

2 PSNR 34.31 32.66 34.12

3 EPI 0.12 0.12 0.38

From table 3 it is evident that the developed fuzzy filter



table that the developed fuzzy filter provides highest amount

of edge preservation during third image denoising.

V. CONCLUSIONS

In this modern era during image acquisition, transmission and

reception, the images are highly influenced by different source

of noises. Hence for proper interpretation of image

information the images must de-noised in all the three stages. In this work a robust and efficient gray image denoising

technique has been successfully developed using window

based soft fuzzy filter with asymmetrical triangular

membership function. Although good denoising techniques are

already available for image denoising like median filter and

average filter, while most of time it has been found that all

these filters provide good results but not able to preserve

image edges during denoising. Ie the resultant images from

conventional techniques are highly blurred. Since edges are

very important characteristics it must be preserved during

denoising process.

Section ~ 4 shows the results obtained after denoising

of three images using developed technique, median filter and

average filter. From the tables it is clearly evident that for all

three images MSE obtained for fuzzy filter is less as compare

to conventional filters for the two different types of noises, on

the other side the PSNR value is higher for fuzzy filter as

compare to conventional filter.

In addition to this the most important task of the developed

algorithm is to preserve the image edges during denoising

process. From the result tables it is clear that the edge

preservation index (EPI) for fuzzy filter is 50% higher than for

median filter and average filter.

For analysis purpose only salt & pepper noise has

been utilized, in future this analysis can be extended for other

type of noises.

REFERENCES

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[11] Amaninder Kaur Brar, Vikas Wassan, “ Image Denoising Using

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4, April 2014, ISSN: 2277 128X, 2014, IJARCSSE.

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Volume 1, Issue 5, May 2014. ISSN 2348 – 4853.

AUTHOR PROFILE

Hemant Kumar is a M.Tech. Scholar of RCET,

Bhilai (C.G.), India. He did his M.C.A. From

Chhattisgarh Swami Vivekananda Technical

University Bhiali, Chhattisgarh

Mr. Dharmendra Kumar Roy is working as

Reader in Department of Computer Science &

Engg., RCET, Bhilai (Chhattisgarh), India. He has

published much research paper in international

journals and presented several research papers in

international conferences.

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