IRJET- SEPD Technique for Removal of Salt and Pepper Noise in Digital Images

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2786
SEPD Technique for Removal of Salt and Pepper Noise in
Digital Images
Dr. Manjunath M1, Prof. Venkatesha G2, Dr. Dinesh S3
1Assistant Professor, Department of ECE, Brindavan College of Engineering, Bangalore, Karnataka, INDIA.
2Professor & HOD, Department of ECE, Brindavan College of Engineering, Bangalore, Karnataka, INDIA.
3Associate Professor & HOD, Department of ISE, Brindavan College of Engineering, Bangalore, Karnataka, INDIA.
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Abstract - The Salt and Pepper noise alsocalledimpulsenoise
is caused by sharp, sudden disturbances intheimagesignal. Its
appearance is randomly scattered white or black (or both)
pixels over the image. The principal source of impulse noise in
digital image arises during image acquisition and
transmission. In this paper, an efficient VLSI implementation
for removing impulse noise is presented. Our extensive
experimental results show that the proposed technique
preserves the edge features and obtains excellent
performances in terms of quantitative evaluation and visual
quality. The design requires only low computational
complexity and two line memory buffers. It’s hardware cost is
quite low. Compared with previous VLSI implementations, our
design achieves better image quality with less hardware cost.
Keywords: Image denoising, impulse noise, VLSI, two line
buffer, SEPD.
1. INTRODUCTION
Applications such as printing skills, medical imaging,
scanning techniques, and image segmentation, and face
recognition, images are often corrupted by noise in the
process of image acquisition and transmission. Hence, an
efficient denoising technique is very important forthe image
processing applications. Digital image processing has many
significant advantages over analog image processing. Image
processing allows a much wider range of algorithms to be
applied to the input data and can avoid problems such as the
build-up of noise and signal distortion during processing of
images. Image noise is the random variation of brightness or
color information in images produced by the sensor and
circuitry of a scanner or digital camera. Image noise can also
originatein film grain and in the unavoidable shotnoiseofan
ideal photon detector. The types of noisesare amplifiernoise
(Gaussian noise), salt-and-pepper noise, shot noise (Poisson
noise), speckle noise. The paper is mainly considered with
removal of fixed value impulse noise. Impulse noise also
called as salt and pepper noise occurs during image
acquisition in an image containing salt-and-peppernoisewill
have dark pixels in bright regions and bright pixels in dark
regions i.e, during analog to digital conversion and in bit
transmission. For an 8-bit digital image, the impulse noise
which occurs as bright spots over dark background and dark
spots over bright background takes a value of 0 and 255 i.e.,
the minimum and maximum value in the greyscale.Hencean
efficient denoising technique is required for denoising. The
paper proposes efficient impulse noise removal architecture
with less computation complexity. For real-time embedded
applications, the VLSI implementation of switching median
filter for impulse noise removal is necessary and should be
considered.ForCustomers,costisusuallythemostimportant
issue while choosingconsumer electronicproducts.Wehope
to focus on low-cost denoising implementation in this paper
.The cost of VLSI implementation depends mainly on the
required memory and computational complexity.Hence,less
memory and few operations are necessary for a low-cost
denoising implementation. Based on these two factors, a
simple edge- preserved denoising technique (SEPD) and its
VLSI implementation forremoving fixed-valueimpulsenoise
are presented. The storage space needed forSEPD is two line
buffers rather thana full frame buffer.Onlysimplearithmetic
operations, such as addition and subtraction, are used in
SEPD.
II. IMPULSE NOISE REMOVAL METHODS
Over the years, better noise removal methods with
different kinds of noise detectors have been proposed.
Several non linear filters have been proposed for the
restoration of images corrupted with impulse noise. There is
a need to develop a filter which are not only effective in
removing impulse noise but also preserve the edges or high
frequency area of image. Therefore the use of nonlinear
filtering techniques came into existence and a class of widely
used non-linear digital filters is median filters and
morphological filters. In [8], Zhang and Karim proposed a
new impulse detector (NID) for switching median filter. NID
used the minimum absolute value offourconvolutionswhich
are obtained by using 1-D Laplacianoperatorstodetectnoisy
pixels. A method named as differential rankimpulsedetector
(DRID) is presented in [9]. The impulse detector of DRID is
based on a comparison of signal samples within a narrow
rank window by both rank and absolute value. In Luo
proposed a method which can efficiently removetheimpulse
noise (ERIN) based on simple fuzzy impulse detection
technique. An alpha-trimmedmean based method(ATMBM)
was presented in [10]. It used the alpha trimmed mean in
impulse detection and replaced the noisy pixel value by a
linear combination of its original value and the median of its
local window. In [11], a decision-based algorithm (DBA) is
proposed to remove the corrupted pixel by the median or by
its neighboring pixel value according theproposeddecisions.
One of the most popular method is the median filter, which

can suppress noise with high computational efficiency [2].
However, since every pixel in the image is replaced by the
median value in its neighborhood, the median filter often
removes desirable details in the image and blurs it too. The
weighted median filter [3] and the center- weighted median
filter[4] were proposed as remedy to improve the median
filter by giving more weight to some selected pixels in the
filtering window. Although these two filters can preserve
more details than the median filter, they are still
implementeduniformlyacrosstheimagewithoutconsidering
whether the current pixel is noise-freeornot.Adaptivefilters
can simultaneously suppress impulses, additive white noise,
and signal-dependent noise. It is noticed that the adaptive
filter is not effective in suppressing impulse noise [5-6]. To
avoid the damage on noise-free pixels, the switching median
filters [7] are used which consists of two steps: 1) Impulse
detection and 2) Noise filtering. It locates the noisy pixels
with an impulse detector, and then filters them rather than
the whole pixels of an image to avoid the damage on noise-
free pixel The rest of this paper is organized as follows. The
proposed SEPD and the VLSI implementation of SEPD is
described in section III. TheExperimentalResultsisprovided
in Section IV. Conclusion is presented in Section V.
III. PROPSED SEPD
Fig.1. 3 x 3 Mask Centered on pi,j
In this method it is assumed that the current pixel to be
denoised is located at coordinate (i,j) and denoted as pi,j and
its luminance values before and after the denoising process
are represented as ƒi,jand 𝑓i,jrespectively. If pi,j is corrupted
by the fixed-value impulse noise, its luminance value will
jump to be the minimum or maximum value in gray scale. In
SEPD technique a 3 x 3 mask W centering is adopted for
image denoising as shown in Fig.1. InthecurrentW,thethree
denoised values at coordinates (i-1,j-1),(i-1,j) and (i-1,j+1)
are determined at the previous denoisingprocess,andthesix
pixels at coordinates (i,j-1),(i,j), (i,j+1), (i+1,j-1), (i+1,j) and
(i+1,j+1) are not denoised yet, as showninFig.1.Usingthe3x
3 values in W, it will determine whether pi,j is a noisy pixelor
not. If positive, SEPD locates a directional edge existing in W
and uses it to determine the reconstructed value 𝑓i,j
otherwise 𝑓i,j=ƒi,j.
SEPD is composed of three components: Extreme data
detector, Edge-oriented noise filter and Impulse arbiter. The
extreme data detector detects the minimum and maximum
luminance values in W, and determines whether the
luminance values of and its five neighboring pixels are equal
to the extreme data. By observing the spatial correlation, the
edge- oriented noise filter pinpoints a directional edge and
uses it to generate the estimated value of current pixel.
Finally, the impulse arbiter brings out the proper result. The
Flow chart is as shown in the below Fig.2. Image Data Base
(Pixel Values of the image) is extracted by using imread
command in Matlab and those Pixel ValuesaregiveninSetof
3 x 3 mask forprocessing to the SEPD architecture. Thethree
components of SEPD are described in detail in the following
subsections.
A). EXTREME DATA DETECTOR
The extreme data detector detects the minimum and
maximum luminance values (MINinW andMAXinW)inthose
processed masks from the first one to the current one in the
image. If a pixel is corrupted bythefixed-valueimpulsenoise,
its luminance value will jump to be the minimum or
maximum value in gray scale. If ƒi,j is not equal to MINinW/
MAXinW, it is concluded that pi,j is a noise-free pixel and the
following steps for denoisingpi,j are skipped. If ƒi,j is equal to
MINinW or MAXinW, we set the variable φ to 1, to check
whether its five neighboring pixels are equal to the extreme
data, and store the binarycompared results intoBascanalso
be seen in the pseudo code in Fig.6.
B). EDGE-ORIENTED NOISE FILTER
To locate the edge existed in the current W, a simple edge
catching technique which can be realized easily with VLSI
circuit is adopted. To decide the edge, 12 directional
differences, from D1 to D12 are considered as shown in
Fig.3.Only those composed ofnoise-free pixels are takeninto
account to avoid possible misdetection. If a bit in B isequalto

1, it means that the pixel related to the binary flag is
suspected to be a noisy pixel. Directions passing through the
suspected pixels are discarded to reduce misdetection. In
each condition, at most four directions are chosen for low-
cost hardware implementation. If there appear over four
directions, only four of them are chose according to the
variation in angle. Fig.4. shows the mapping table between B
and the chosen directions adopted in the design. Since five
neighboring pixels are considered32combinationsaretaken
in account for denoising process.
Fig.3. Twelve directional differences of SEPD
Fig.4. Thirty-two possible values of B and their
corresponding directions in SEPD.
If pi,j-1, pi,j+1, p1+1,j-1,pi+1,j and pi+1,j+1 are all suspected
to be noisy pixels (B=“ 11111”) , no edge can be processed,
so i,j(the estimated value of ) is equal to the weighted
average of luminance values of three previously denoised
pixels and calculated as (i -1,j-1+2xi -1,j+ i -1,j+1)/4.Inother
conditions except when B =“11111” theedgefiltercalculates
the directional differences of the chosen directions and
locates the smallest one (Dmin) among them. The smallest
directional difference implies thatithasthestrongestspatial
relation with pi,j, and probably there exists an edge in its
direction. Hence, the mean of luminance values of the two
pixels which possess the smallest directional difference is
treated as i,j. For example, if B is equal to “10011,” it means
that fi,j-1 , fi+1,j and fi+1,j+1 are suspected to be noisy
values. Therefore, D2-D5, D7and D9-D11 are discarded
because they contain those suspected pixels (see fig.3) The
four chosen directional differences are D1, D6, D8 and D12
(see Fig.4). Finally is equal to the mean of luminance values
of the two pixels which possess the smallest directional
difference among D1, D6, D8 and D12.
C). IMPULSE ARBITER
Since the value of a pixel corrupted by the fixed-value
impulse noise will jump to be the minimum/maximumvalue
in gray scale, it is concluded that pi,j is corrupted, fi,j is equal
to MINinW or MAXinW . However, the converse is not true.
in cases where the pixel might not be corrupted by fixed
value impulse noise but might be in the region of minimum
or maximum luminance i.e.,theminimumormaximumvalue
in W might be identified as a noisy pixel. In order, to avoid
the possible misdetection of pixel an impulse arbiter with
spatial threshold is proposed. Since, threshold is an
important consideration in any system an appropriate
threshold can produce better result. If pi,j is a noise-free
pixel and the current mask has high spatial correlation, fi,j
should be close to and |fi,j -𝑓 i,j| is small. That is to say, pi,j
might be a noise-free pixel but the pixel value is MINinW or
MAXinW if |fi,j-𝑓 i,j | is small. |fi,j -𝑓 i,j| is measured and
compared it with a threshold to determine whether is
corrupted or not. The threshold, denoted as Ts, is a
predefined value. If pi,j is judged as a corrupted pixel, the
reconstructed luminance value 𝑓i,j is equal to 𝑓 i,j;
otherwise; 𝑓i,j=ƒi,j. However, it is not easy to derive an
optimal threshold through analytic formulation If the
threshold value is greater than the difference, then the
denoised value is taken as the reconstructed value else the
original value is retained.
The output of the impulse arbiter is fed back as feedback to
the first stage, to process other pixels as seen in Block
diagram (Fig.5). The corrected pixel value is given back to
the line buffers through mux, so that according to the
position of the pixels it is given to even or odd buffers and
through mux it is replaced in the register bank. A new set of
pixel values is fed to the extreme data detector and the
process continues to obtain a noise-free image.

Fig.5.Block Diagram of VLSI Architecture for SEPD
/*Input image size : row(height) x col(width)*/ if
for( i = 0; i < row; i = i+1)
{
for(j = 0; j < col; j = j+1)
{
/*Extreme data detector*/
Get W, the 3 x 3 mask centered on ( i,j); Find MINinW and
MAXinW;
/*the minimum and maximum values from the first W to the
current W*/
φ=0; /*initial values*/
if ((ƒi,j = MINinW) or (ƒi,j=MAXinW))
φ=1; /* pi,j is suspected to be a noisy pixel*/ if (φ=0)
{ i.j=ƒi,j; break;} /* pi,j is a noisy-free pixel*/
B=b1b2b3b4b5=”00000”; /*initial values*/ If ((ƒi,j-
1=MINinW)or(ƒi,j-1=MAXinW))
b1=1; /*pi,j-1 is suspected to be a noisy pixel*/ if
((ƒi,j+1=MINinW)or(ƒi,j+1=MAXinW))
b2=1; /*pi,j+1 is suspected to be a noisy pixel*/ if ((ƒi+1,j-
1=MINinW)or(ƒi+1,j-1=MAXinW))
b3=1; /*pi+1,j-1 is suspected to be a noisy pixel*/ if
((ƒi+1,j=MINinW)or(ƒi+1,j=MAXinW))
b4=1; /*pi+1,j is suspected to be a noisy pixel*/ if
((ƒi+1,j+1=MINinW)or(ƒi+1,j+1=MAXinW))
b5=1; /*pi+1,j+1 is suspected to be a noisy pixel*/
/*Edge-Oriented Noise Filter*/
Use B to determine the chosen directions across pi,j
according to fig,4;
if (B=”11111”)
/*no edge is considered*/
�i,j=(�i -1,j-1 + 2x�i -1,j +�i -1,j+1)/4; else
{ find Dmin (the smallest directional difference among the
chosen directions);
�i,j=the mean of luminous value of the twopixelswhichown
Dmin;}
/*Impulse Arbiter*/
if (‫׀‬ƒi,j-�i,j‫׀‬ > Ts)
�i,j=�i,j; /*pi,j is judged as noisy pixel*/ else
�i,j=ƒi,j; /*pi,j is judged as noisy-free pixel*/
}
}
IV. EXPERIMENTAL RESULTS
Fig.6.Pseudo Code for SEPD Technique
The PSNR(Peak signal to noise ratio) of the above image by
using SEPD technique is 35.16 and MSE(mean square error)
is 19.818.The Simulation Results obtained using Model Sim
for Verilog coding ,for the above SEPD technique is as
follows:
1).When all the pixels including Pi,j in W:3X3 mask are
noisy,(B=”11111”).
2).When pi,j is noisy ,for any condition of B.
(Eg. shown is for B=”01100”)
3).When pi,j is not noisy ,for any condition of B.
(Eg. shown is for B=”00000”)

V. CONCLUSIONS
By the implementation of the proposed algorithm described
in this paper, it is possible to suppress the impulse noise in
an efficient way by retaining the original image’sfinedetails.
It requires less memory and few operations and achieves
excellent performance in terms of quantitative evaluation
and
visual quality even if the noise ratio is high. By which this
method will reduce the hardware cost and computational
complexity, thus helpful for any real-time embedded
applications. It provides higher filtering quality and better
performance than the existing techniques. Thearchitectures
work with monochromatic images, but they canbeextended
for working with RGB color images and videos.
REFERENCES
[1] W. K. Pratt, Digital Image Processing. New York: Wiley-
Inter-science, 1991.
[2]T. Nodes and N. Gallagher, “Median filters: Some
modifications and their properties,” IEEE Trans. Acoust.,
Speech, Signal Process., vol.ASSP-30, no. 5, pp. 739–746,Oct.
1982.
[3] D. R. K. Brownrigg, “The weighted median filter,” Comm.
ACM, vol. 27, pp. 807-818, Aug. 1984.
[4] S.-J. Ko and Y.-H. Lee, “Center weightedmedianfiltersand
their applications to image enhancement,” IEEE Trans.
Circuits Syst., vol. 38, no. 9, pp. 984–993, Sep. 1991.
[5] H. Hwang and R. Haddad, “Adaptive median filters: New
algorithms and results,” IEEE Trans. Image Process., vol. 4,
no. 4, pp. 499–502, Apr. 1995.
[6] I. Andreadis and G. Louverdis, “Real-time adaptiveimage
impulse noise suppression,” IEEE Trans. Instrum. Meas.,vol.
53, no. 3, pp. 798–806, Jun. 2004.
[7] Rajoo pandey,”An improved Switching Median Filter for
Uniformly Distributed Impulse Noise removal,” World
Academy of Science, Engineering and technology ,2008.
[8] S. Zhang and M. A. Karim, “A new impulse detector for
switching median filter,” IEEE Signal Process. Lett., vol. 9,no.
11, pp. 360–363, Nov. 2002.
[9] I. Aizenberg and C. Butakoff, “Effective impulse detector
xbased on rank-order criteria,”IEEESignal Process.Lett.,vol.
11, no. 3, pp. 363–366, Mar. 2004.
[10] Manjunath M, Dr H B Kulkarni “Analysis of Unimodal
and Multimodal BiometricSystemusingIrisandFingerprint”
Perspectives in Communication, Embedded-Systems and
Signal-Processing (PiCES) – An International Journal ISSN:
2566-932X, Vol. 2, Issue 8, November 2018.
[11] W. Luo, “Efficient removal of impulse noise from digital
images,” IEEE Trans. Consum. Electron., vol. 52, no. 2, pp.
523–527, May 2006.
[12] W. Luo, “An efficient detail-preserving approach for
removing impulse noise in images,” IEEE Signal Process.
Lett., vol. 13, no.7, pp.413–416, Jul. 2006.
BIOGRAPHIES
Dr. Manjunath M is Assistant Professor in
Department of Electronics
&Communication Engineering, Brindavan
College of Engineering, Bangalore,
Karnataka, INDIA. He obtained his B.E. In
Electronics and Communication
Engineering and M.Tech in Signal
Processing from Visvesvaraya
Technological University, Belgavi,
Karnataka, INDIA. He has been awarded Ph.D. in Electronics
and Communication Engineering. He has 30 research
publications in refereed International Journals and
Conference Proceedings. His research interests include
Image Processing, Biometrics, Audio & speech processing,
Statistical signal Processing, Artificial Intelligence and
Machine learning etc.
Prof. Venkatesha G is ProfessorandHOD,
Department of Electronics
&Communication Engineering, Brindavan
College of Engineering, Bangalore,
Karnataka, INDIA. He obtained his B.E. In
Electrical & Electronics Engineering from
Mysore University and M.S. in Electronics
and Controls from BITS, Pilani, INDIA. He
is Pursuing his Ph.D. in Electrical Sciences.
He has 13 research publications in
refereed International JournalsandConferenceProceedings.
His research interests include Control Systems, Image
Processing, Artificial Intelligence and Machine Learning etc.

Dr. Dinesh S is Associate Professor and
HOD, Department of Information Science
& Engineering, Brindavan College of
Engineering, Bangalore. He obtained his
B.E. In Computer Science and Engineering
and M.Tech in Software Engineering from
Visvesvaraya Technological University,
Belgavi, Karnataka, INDIA. He has been
awarded Ph.D. in Computer Science and
Engineering. He has 10 research publications in refereed
International Journals and Conference Proceedings. His
research interests include Computer Networks, Image
Processing, Computer graphics, Artificial Intelligence and
Machine Learning etc.

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