fuzzy logic based adaptive image denoising -...

Fuzzy Logic Based Adaptive Image Denoising

Monika Sharma

Baba Banda Singh Bhadur Engineering College,

Fatehgarh,Punjab (India)

SarabjitKaur Sri Sukhmani Institute of Engineering &

Technology ,Derabassi,Punjab (India)

Derabassi,Punjab (India)

Er.Jatinderpal Singh Raina, Baba Banda Singh Bahadur Engineering College,

FatehgarhSahib,Punjab (India) [email protected]

Abstract- In this paper we proposed a new fuzzy logic based approach that detects and removes random valued salt and pepper noise in gray scale digital images. Proposed work is done in two steps, in first step we detect noisy pixels using fuzzy logic, and in second step we replace those noisy pixels using our fuzzy based approach. Our algorithm also includes the histogram based approach for noise removal from the image. We analyze our method for image parameters like PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error). To illustrate the proposed method, experiments have been performed on standard gray level test image like Lena and results are compared with other popular image denoising methods. The resultsshow that the proposed methodhas relatively good performance with desirable improvement in the PSNR and MSE of the image

Keywords-Impulse Noise; Fuzzy Logic; image denoising; PSNR.

1. Introduction Digital gray scale images obtained through various

digital products are often corrupted by impulse noise

during image acquisition, transmission and reception.

Attenuation of noise and preservation of details are

two aspects of image processing [2]

. Three important

types of noise exist: impulse noise, multiplicative

noise and additive noise, however, the most

annoying noise, which can degrade the image quality

is impulse noise. As we know there are many

algorithms available for reduction of each type of

noise. For example standard median filter (SMF)

that is based on order statistic and is nonlinear filter [1]

removes the impulse noise but it mistakenly destroys the edges as it is accustomed in replacing

the pixels which are undisturbed by noise.. Other

methods available are: Rank conditioned median

filters (RCM) [4]

in which pixels in the filtering

window are ranked according to their magnitudes,

center weight median filter (CWMF) [1]

which

emphasis or deemphasize specific input samples,

Arak Fuzzy median filter (AFMF)[1]

in which the

computation time is a compromise and (FBMF)

Fuzzy based median filter. Most of these algorithms

provide suitable and good results at smaller

percentage of noise levels and find difficulty with

higher level noises. But our proposed fuzzy based

approach for impulse noise removal gives better

image quality improvement than above mentioned

methods.

2. Proposed method – Fuzzy logic based

adaptive filter A gray scale image is represented by a two-

dimensional array where a location (i, j) is a position

in image and called pixel. Often the gray scale image

is stored as an 8-bit integer giving 256 possible

different shades of gray going From black to white

pixels can have value in [0-255] integer interval, but

some pixels in an image have not correct value and

they represent noise having values 0 or255, this noise

is known as salt and pepper noise. Salt and pepper noise is not a spreaded noise. We can show basic

image denoising model for a gray scale image which

removes impulse noises such as 255(pepper) and

0(salt).

Figure 1: Basic Image Denoising Model

In our context noise detection and removal

mechanism will work according to the following

steps:

1.3X3 filter mask for removing low level noise.

2. 3X2 filter mask for removing noise from corners and edges.

3. 4X4 filter mask for removing high level noise.

4. Histogram scanning method for further improving

the image by enhancing PSNR.

Fuzzy logic means to predict data using metadata i.e.

to predict something using different probabilities.

Here we are considering random valued salt and

pepper noise, the filter used is mean filter and we are

applying local scanning to the tested image. First of

Monika Sharma et al , Int.J.Computer Technology & Applications,Vol 4 (3),502-507

IJCTA | May-June 2013 Available [email protected]

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ISSN:2229-6093

all to detect the noise present in the image we have to

find out the values of pixels and analyze the

difference of that particular pixel value with

itsneighboring pixels values. Normally the difference

is large in case of salt and pepper noise. To remove

this noise we are using a 3x3 filter mask which

will scan the whole image and replace the value of

pixel with the mean of neighboring ones. During the

scanning process if the vote for a particular pixel is greater than the threshold value then it will take that

pixel as noise. This scanning matrix removes only

low level noise from the image. Since 3X3 scanning

matrix won’t be able to remove the noise from

corners and edges of the image as it won’t scan first

row, first column, last row and last column therefore

we have to use a scanning matrix of the order 3X2

for the same which will remove noise from the

corners and edges of the image. This 3X2 scanning

makes our algorithm different from the already

existing methods. After that 4X4 scanning is done

for removing high level noises which occurs due to

the increase in the percentage of noise. At last

histogram scanning is applied on the image to

remove the leftover noise, still present in the image

after applying first two scanning. The histogram tells

the number of pixels of a specific value. The horizontal axis of graph represents the tonal

variations while vertical axis represents number of

pixels in that particular tone. In histogram scanning

method we are assuming that the pixels having low

values are noise and by selecting a threshold level we

will remove the noisy pixels from the image by

calculating the mean and using the mean method of

filtering. This assures good PSNR and MSE

compared to other methods.

3. Implementation and Results

We have used MATLAB 7 for implementation and

analysis of the results for standard LENA image.

Results show better performance of proposed method

over other ones as shown in table 1 and we

analyzetwo parameters:-

1. Peak Signal to Noise Ratio (PSNR)

2. Mean Square Error (MSE)

PSNR is given by

PSNR= 10𝑙𝑜𝑔10 2552 𝑀𝑆𝐸 𝑑𝐵

Where MSE is given by

𝑀𝑆𝐸 =1

𝑚𝑛 ( 𝐼 − 𝐾)2

𝑛

𝑗=0

𝑚

𝑖=0

𝑝𝑖𝑥𝑒𝑙

Where for MSE

I is input Image i.e. noisy image

K is output image after applying proposed algorithm.

After the implementation of algorithm on the 8 bit

gray scale Lena image of size 512x512 results are

shown in figure 2 to figure 5.Also the results have

been plotted graphically in figure 6.

The results on the LENA image are shown in table 1

where the noise ratio for random valued salt and

pepper noise ranges from 5% to 40%. The results

from the Table 1 are plotted graphically in Figure 4and it is clear from the graph that the used filter

with fuzzy based approach outperforms all the other

filters in terms of parameters which we are

considered for evaluation.

4. Conclusion

Hence it is concluded that proposed fuzzy based

image denoising algorithm is able to suppressand

removes the salt and pepper noise in digital gray

scale image while preserving details across a wide

range of impulse noise corruption and gives better

performance than other methods such as standard Median, CWM, RCM etc.The future scope is to

extend this fuzzy algorithm for the coloured images.

References

[1] Kh. Manglem Singh “Fuzzy Rule based Median Filter for Gray-scale images Journal of

Information Hiding and Multimedia Signal

Processing” April 2011

[2] HaixiangXu, XiaoruiYue “An Adaptive Fuzzy Switching Filter for Images Corrupted by

Impulse”2009 Sixth International Conference on

Fuzzy Systems and Knowledge Discovery

[3] ZHANG Hong-qiao, MA Xin-jun, WU-Ning “A New Filter Algorithm of Image Based on Fuzzy

Logical” 2011 IEEE

[4] Kh. Manglem Singh and Pravin K. Bora “

Improved rank conditioned filter for removal of impulse noise from the images” IEEE 2002

[5] Mahmoud Saeidi, M. HasanMoradi,

FatemehSagafi“Filtering Image Sequences

Corrupted by Mixed Noise using a New Fuzzy Algorithm” 2006 IEEE

[6] Tonghan Wang, Xingyi Li “An Efficient Impulse

Noise Reduction Algorithm” 2011 IEEE

[7] HaixiangXu, XiaoruiYue “An Adaptive Fuzzy Switching Filter for Images Corrupted by

Impulse Noise”2009



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Table 1: Comparative PSNR performance of different methods in filtering Lena image corrupted with 5% to 40% random valued impulse noise

Noise

% SM CWM RCM Neuvo Arak FBMF

Before applying

our Algorithm

After

applying our

Algorithm

PSNR PSNR PSNR PSNR PSNR PSNR PSNR MSE PSNR MSE 5 33.64 36.47 35.77 38.03 36.9 38.98 25.78 165.56 41.21 5.01

10 32.82 34.88 34.61 35.53 35.5 36.92 22.82 335.95 40.41 5.95

15 31.78 33.4 33.46 33.75 34.09 35.35 21.11 505.23 39.73 6.70

20 30.93 32.29 32.57 32.2 33.05 34.05 19.98 661.24 38.92 8.18

25 29.88 30.68 31.74 31.16 31.76 33.06 18.9 835.27 38.20 9.65

30 28.69 29.39 30.62 29.98 30.65 31.76 18.2 999.03 37.64 11.33

35 27.69 28.1 29.87 29.11 29.6 30.41 17.43 1176.00 36.79 13.75

40 26.91 26.44 29.81 27.9 28.75 28.88 16.9 1325.50 33.11 30.41

Figure 2: Original Lena image Figure 3: Lena image corrupted with 30% random valued impulse noise

Figure 4: Output of 3x3 & 3x2 Fuzzy logic based scanning matrix



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Figure 5: Output of 4x4 fuzzy logic based scanning matrix filter & its histogram showing the

noisy pixels with small values on extreme left and right

0

1000

2000

3000

0 50 100 150 200 250



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Figure 6: Output of Histogram based Fuzzy logic scanning filter and its corresponding histogram which shows that noisy pixels has been eliminated

0

1000

2000

3000

0 50 100 150 200 250



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Figure 7: Graphical Plot showing the comparison of different filters with our proposed fuzzy based filter

5 10 15 20 25 30 35 400

10

20

30

40

50

60

70

80

percentage of impulse noise

PS

NR

in d

B

PSNR Vs percentage of impulse noise

sm

cwm

rcm

neuvo

arak

prop

our



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