International Journal of Advanced Computer Engineering and Communication Technology (IJACECT)
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ISSN (Print): 2278-5140, Volume-2, Issue – 3, 2013
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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
International Journal of Advanced Computer Engineering and Communication Technology (IJACECT)
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ISSN (Print): 2278-5140, Volume-2, Issue – 3, 2013
28
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.
International Journal of Advanced Computer Engineering and Communication Technology (IJACECT)
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ISSN (Print): 2278-5140, Volume-2, Issue – 3, 2013
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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)
Cameraman image Corrupted by 30% noise
(a)
(b)
(c)
(d)
(e)
Cameraman image Corrupted by 50% noise
(a)
(b)
(c)
(d)
(e)
Cameraman image Corrupted by 80% noise
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.
International Journal of Advanced Computer Engineering and Communication Technology (IJACECT)
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ISSN (Print): 2278-5140, Volume-2, Issue – 3, 2013
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(a)
(b)
(c)
(d)
(e)
Lena image Corrupted by 10% noise
(a)
(b)
(c)
(d)
(e)
Lena image Corrupted by 30% noise
(a)
(b)
(c)
(d)
(e)
Lena image Corrupted by 50% noise
(a)
(b)
(c)
(d)
(e)
Lena image Corrupted by 80% noise
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 .
International Journal of Advanced Computer Engineering and Communication Technology (IJACECT)
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ISSN (Print): 2278-5140, Volume-2, Issue – 3, 2013
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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
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