IRJET- Salt and Pepper Noise Removal using Decision based Filters

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 6837
Salt and Pepper Noise Removal using Decision Based Filters
Vinayak C. Sakhare1, Sachin V. Kamble2, Jakia A. Alase3, Anil P. Athane4
1Assistant Professor, Department of Electronics, Textile & Engineering Institute, Ichalkaranji, Maharashtra, India
2Assistant Professor, Department of I.T., Textile & Engineering Institute, Ichalkaranji, Maharashtra, India
3Assistant Professor, Department of E.T.C., Textile & Engineering Institute, Ichalkaranji, Maharashtra, India
4Assistant Professor, Department of Electronics, Textile & Engineering Institute, Ichalkaranji, Maharashtra, India
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Abstract - Images are often corrupted by salt and pepper
noise causing loss of information. Hence its removal is
important. Numerous filters like median filter, adaptive
median filter, switching median filter, decision based filters
and their variants have been proposed by variousresearchers.
Noise detection scheme, quantitative & qualitative
performance andexecutiontimeareimportantparameters for
filters. Based on these parameters, decision based filters are
performing well in terms of quantitative measures, quality of
filtered image and execution time. In this paper review of
decision based filters is carried out.
Key Words: Salt, Pepper, Impulse, Median, Decision based
1. INTRODUCTION
An image carries large amount of information and isa tool of
communication between people. However unfortunately
sometimes image will be corrupted byparasitic information,
which is called: Noise, an alterationofimage, whichmaylead
to loss of information. Hence removal of noise is important
for extraction of reliable & accurate information and also
before subsequent image processing operations like image
segmentation, edge detection, object recognition etc. Digital
images are corrupted by various types of noise such as
Gaussian noise, Rayleigh noise, Erlang noise, exponential
noise, uniform noise and impulse noise. Out of these types,
the impulse noise has tendency of being either relatively
high or relatively low and it is arising due to the errors in
image acquisition (faulty sensors), faulty memory locations
and transmission through noisy channel. Impulse noise is
having two types viz; salt and pepper noise and random
valued impulse noise. Salt-and-pepper noise is a type of
impulse noise which alters some of the pixel values either at
its extreme maximum (255) or at its extreme minimum (0)
to make them as white and black dots. Instead of two fixed
extreme values, random valued impulse noise is more
realistically modelled as two fixedrangesthatappearatboth
ends with a certain length.
The objective of digital filter is to identify corrupted pixels
and replace them with a value which will be equal to closeto
the true value. Filters compute these values using some
predefined relationship. Filters usedfortheremoval ofnoise
from corrupted image operate either in spatial domain or in
frequency domain. The term spatial domain refers to the
aggregate of pixels composing the image. Spatial domain
methods are procedures that operate directly on the pixels.
Large number of filters have been proposed to recover
images corrupted by salt and pepper noise at various noise
density levels. These filters are mainly of two types namely
linear and nonlinear. Performance of nonlinear filters is
found to be better than linear filters for removal of salt and
pepper noise.
Standard median filter is a simple nonlinear and reliable
filter to remove the salt-and-pepper without damaging
edges. The main disadvantage of median filter is, it applies
median filtering scheme to each pixel without considering
whether pixel is corrupted or not. So Adaptive median filter
(AMF) uses noise detection scheme. It provides good results
for low densities of noise but for high density of noise
window size is to be increased which may lead to blurring in
image. In switching median filters the decision of corrupted
image pixel is based on a pre-defined threshold value.Asthe
threshold value affects the performance of impulse
detection, it is necessary to derive an optimal threshold. In
decision based filter, the pixel is deemed as corrupted if its
value is either ‘0’ or ‘255’ otherwise the pixel isuncorrupted.
Due to this simple noise detection scheme, decision based
filter is faster and hence large number of variantsofdecision
based filters have been proposed by various researchers.
Review of such recent filters is taken in this paper.
2. REVIEW OF DECISION BASED FILTERS
A New Fast and Efficient Decision-Based Algorithm for
Removal of High-Density Impulse Noises is proposed by K. S.
Srinivasan et al. in [1]. In this algorithm, pixels having
minimum (0) and maximum values (255) are deemed as
corrupted. Such pixels are replaced by median of 3 * 3
neighborhood window provided that median itself is not
noise (0 or 255) otherwise pixels are replaced by last
processed neighborhood value. This filter performs well at
low noise densities. At high noise density streaking occurs
due to replacement with neighborhood pixel value [8].
Removal of Salt-and PepperNoiseinImages:ANewDecision-
Based Algorithm proposed by Madhu S. Nair etal.[2],utilizes
impulse noise detection scheme of AMF and processes
corrupted pixels by median of neighborhood window of 3 *3
size or by mean of previouslyprocessed neighborhoodpixels
if median happens to be noisy pixels. This algorithm is faster
and efficient at low noise density. At high noise density, it
happens that median value is coming from a noisy pixel
which leads to streaking effect [11].

Removal of High Density Salt and Pepper Noise through
Modified Decision Based Unsymmetric Trimmed Median
Filter (MDBUTMF) is proposed by S. Esakkirajan et al. in [3].
In this filter, every corrupted pixel is processed by 3 * 3
neighborhood window. If all neighboring pixels are 0’s and
255’s then current pixel is replacedbymeanofneighborhood
window else if not all neighboring pixels are 0’s and 255’s
then current pixel is replaced by median of neighboring
uncorrupted pixels. At high noise density, this algorithmfails
if all the elements within the window are ‘0’ or ‘255’ then the
mean value will be ‘0’or ‘255’ hence noisy pixel will not be
efficiently processed [5].
A Decision-based Filter for Removing Salt-and-Pepper Noise
is proposed by Muhammad Mizanur Rahman et al [4]. This
Non Linear Decision Based Filter (NDBF) also filters only
corrupted pixels (pixels having values 0 or 255). It replaces
corrupted pixel by median of3*3neighborhoodwindowonly
if median value is not one of the extreme gray levels. If
median value is 0 or 255, it checks value of Last Processed
Pixel (LPP). If LPP is noise free then it takes neighborhood
window of 10*10 size and calculates median and mode this
window. If this median is noise free then current pixel is
replaced by LPP else currentpixel is replacedbymode.IfLPP
is noisy pixel, then eight 3*3 sized adjacent windows of
current window are considered. Modes of these 9 windows
are calculated and then current pixel is replaced by mode of
these 9 mode values. In this way all corrupted pixels are
filtered and finally robust estimator is applied on filtered
image for enhancement.
Md Tabish Raza et al. have proposed High Density Salt and
Pepper Noise Removal through Decision Based Partial
Trimmed Global Mean Filter (DBPTGMF) in [5].Itovercomes
drawback of MDBUTMF. It also uses 3 * 3 neighborhood
window to filter each noisy pixel having value 0 or 255. If
neighborhood window contains all elements as 0 then
current pixel is replaced by salt noise (255) trimmed global
mean value of image else if neighborhood window contains
all elements as 255 then current pixel is replaced by pepper
noise (0) trimmed global mean value of image else if
neighborhood window contains all elements as 0 and 255
both then current pixel is replaced by salt and pepper noise
(both 0 and 255) trimmed global mean value of image else
current pixel is replaced by median of uncorrupted
neighboring pixels. This methods give excellent result at low
and medium noise densities. But at high noise density i.e.
above 60% noise it has a poor IEF value and lead to blurring
the image [8].
Satyabrata Biswall et al. have proposed decision based
algorithm in [6]. In this algorithm, noisy pixel is replaced by
mean of neighboring pixels if all neighbors are corrupted. If
some neighboring pixels are 0 or 255 or both then these
corrupted neighboring pixels are trimmed and current pixel
is replaced by mean of remaining noise free pixels.
Fast and efficient median filter for removing 1–99% levels of
salt-and-pepper noise in images is proposed by Mu-Hsien
Hsieh et al. in [7]. Like other filters, this filter also deems
pixels having very large value (positive impulse) or very
small value (negative impulse) as noisy pixel. Then this filter
calculates noise density to select type of neighborhood
window to be employed for filtering. If noise density is less
than 50% then it uses simplified type of window in which
only horizontal and vertical neighbors of current pixel are
considered. Above 50% noise density full type of
neighborhood window having all neighbors is selected.
Beginning with 3,size of neighborhood window is iteratively
increased by 2 until it includes noise free pixels. Finally
corrupted pixel is replaced by median of noise free
neighborhood pixels.
Arabinda Dash et al. have proposed High Density Noise
Removal by Using Cascading Algorithms in [8]. In order to
remove salt and pepper noiseatallnoisedensitylevelsandto
enhance image quality at high noise density level effectively,
authors have cascaded two filtering stages. First stage is
Decision based Median Filter wherein only corrupted pixels
are replaced by median of 3 * 3 neighborhood window. The
output obtained from first stage is given as input to second
stage for further processing. In second stage either Modified
Decision Based Partial Trimmed Global Mean Filter
(MDBPTGMF) or Modified Decision Base Unsymmetric
Trimmed Median Filter (MDBUTMF) is used. In MDBPTGMF,
if all neighbors of corrupted pixel are 0 (pepper noise) then
pixel is replaced by salt noise else if all neighbors of
corrupted pixel are 255 (salt noise) then pixel is replaced by
pepper noise else if some neighbors are 0 and others are 255
then pixel is replaced by mean value of window else if not all
neighbors are 0 and 255 then 0 and 255 are eliminated and
pixel is replaced by median of remaining values. In
MDBUTMF, if all neighbors of corrupted pixel are 0 and 255
then pixel is replaced by mean of the window else 0 and 255
are eliminated and pixel is replaced by median of remaining
pixels. It has been found that as the noise density increases
this algorithms fails to preserve the texture details of the
image i.e. the originality is lost at high noise density [10].
With the intention of maximize the speed without
compromising denoising performance, an extremely fast
adaptive high-performance filter to remove salt and pepper
noise using overlapping medians in images is proposed by G.
Balasubramanian et al. in [9]. This algorithm uses three
stages of filtering. The first filtering stage is Overlapping
Medians Filter (OMF) which is principally asystematicframe
by frame merging of the estimations of standard medians of
size M× N. The frame-based operations in the OMF has been
designed and developed such that it should give the partially
denoised image at an extremely fastrate.Thesecondfiltering
stage is Running Average Filter (RAF) employs anestimation
named as running averages (RA) using which the noisy
samples available in OM, if any, will get denoised at a quicker
rate. Third phase is mean filter which makes useofasimple3
× 3 mean filter to fine tune the RA, if necessary, to get a final
denoised image. At very high noise densities, this mean filter
provides a significant contribution towards improving the
correlations between the denoised samples to give out some
visually pleasant denoised image.
Removal of Salt and Pepper noise from Gray-Scale and Color
Images: An Adaptive Approach is presentedbySujayaKumar
Sathua et al. in [10] to preserve the edgeand texture detail of

the image. It is variation of MDBPTGMF filter. In this filter,
maximum neighborhood window size Wmax selected is 9. It
starts with neighborhood window of size W= 3. It filters all
corrupted pixel as follows. If number of uncorrupted
neighboring pixels N is greater than or equal to W then
current corrupted pixelisreplacedbymedianofuncorrupted
neighborhood pixels. If above condition is not satisfied,
window size W is incremented by two up to Wmax
iteratively. During each iteration, if W < Wmax and N ≥ W
then current pixel is replaced by median of uncorrupted
neighborhood pixels else if W = Wmax, N < W and N ≠ 0 then
current pixel is replaced by mean of uncorrupted
neighborhood pixels else if W = Wmax and all neighboring
pixels are 0 and 255 then current pixel is replacedbymeanof
neighborhood pixels else if W = Wmax and all neighboring
pixels are 255 then current pixel is replaced by 0 else if W =
Wmax and all neighboring pixels are 0 then current pixel is
replaced by 255.
3. CONCLUSION
Removal of impulse noise from noisy image is important to
avoid loss of information. Various decision based filters are
reviewed in this paper. Important parameters for selection
of a filter for noise removal are noise detection scheme,
quantitative & qualitative performance and execution time.
Pixels corrupted with salt and pepper noise are taking
maximum and minimum values. In decision based filters,
pixels having values 0 and 255 are deemed as corruptedand
then various filtering techniques are used to replace those
pixels. Due to simple noise detection scheme decision based
filters are faster than other types of filters and their
performance varies as per filteringtechniqueused.However
if uncorrupted pixels are having values 0 and 255, these are
also filtered unnecessarily leading to false alarms and these
decision based filters are not useful for removal of random
valued impulse noise.
REFERENCES
[1] K. S. Srinivasan and D. Ebenezer, “A New Fast and
Efficient Decision-Based AlgorithmforRemoval ofHigh-
Density Impulse Noises,” IEEESignal ProcessingLetters,
Vol. 14, NO. 3, March 2007, doi:
10.1109/LSP.2006.884018.
[2] Madhu S. Nair, K. Revathy, and Rao Tatavarti, “Removal
of Salt-and Pepper Noise in Images: A New Decision-
Based Algorithm”, Proceedings of the International
MultiConference of Engineers and Computer Scientists
2008 Vol I, IMECS 2008, 19-21 March, 2008, HongKong,
doi:
[3] S. Esakkirajan, T. Veerakumar, Adabala N.
Subramanyam, and C. H. PremChand, “Removal of High
Density Salt and Pepper Noise Through Modified
Decision Based Unsymmetric Trimmed Median Filter”,
IEEE Signal Processing Letters, Vol. 18, No. 5, May 2011,
doi: 10.1109/LSP.2011.2122333
[4] Muhammad Mizanur Rahmani, M. Abdullah-AI-Wadud
and Chrysanthe Prezal, “A Decision-based Filter for
Removing Salt-and-Pepper Noise”, IEEE/OSA/IAPR
International Conference on Infonnatics, Electronics &
Vision 2012, doi: 10.1109/ICIEV.2012.6317513
[5] Md Tabish Raza and Suraj Sawant , “High Density Salt
and Pepper Noise Removal Through Decision Based
Partial Trimmed Global Mean Filter”, 2012 Nirma
University International Conference on Engineering
(NUiCONE), doi: 10.1109/NUICONE.2012.6493236
[6] Satyabrata Biswall and Nilamani Bhoi, “A new filter for
removal of salt and pepper noise”, 2013 International
Conference on Signal Processing, Image Processing and
Pattern Recognition [ICSIPR], doi:
10.1109/ICSIPR.2013.6497990
[7] Mu-Hsien Hsieh , Fan-Chieh Cheng, Mon-Chau Shie,
Shanq-Jang Ruan, “Fast and efficient median filter for
removing 1–99% levels of salt-and-pepper noise in
images”, Engineering Applications of Artificial
Intelligence 26(2013), pp. 1333-1338, doi:
10.1016/j.engappai.2012.10.012
[8] Arabinda Dash and Sujaya Kumar Sathua, “High Density
Noise Removal By Using Cascading Algorithms”, Fifth
International Conference on Advanced Computing &
Communication Technologies 2015, doi:
10.1109/ACCT.2015.100
[9] G. Balasubramanian, A. Chilambuchelvan, S. Vijayanand
G. Gowrison, “An extremely fast adaptive high-
performance filter to remove salt and pepper noise
using overlapping medians in images”, The Imaging
Science Journal, 64:5, pp. 241-252, doi:
10.1080/13682199.2016.1168144
[10] Sujaya KumarSathua,Arabinda Dash andAishwaryarani
Behera, “Removal of Salt and Pepper noise from Gray-
Scale and Color Images: An Adaptive Approach”,
International Journal of Computer Science Trends and
Technology (IJCST) – Volume 5 Issue 1, Jan – Feb 2017,
pp. 117- 126
[11] V.Jayaraj , D.Ebenezer and K.Aiswarya, “High Density
Salt and Pepper Noise Removal in Images using
Improved Adaptive Statistics Estimation Filter”, IJCSNS
International Journal of Computer Science andNetwork
Security, VOL.9 No.11, November 2009, pp. 170-176
BIOGRAPHIES
Vinayak C. Sakhare has completed his ME Electronics
Engineering in 2014 from Textile & Engineering Institute.
His areas of interest are Image Processing, Embedded
Systems.
Sachin V. Kamble has pursuedhisM.Tech.ComputerScience
and Engineering degree in2014fromRajarambapuInstitute
of Technology in 2014. His research area is scheduling and
optimization algorithm, parallel computing.
Mrs. Jakia A. Alase has completed her M. E. Electronics and
Telecommunication degree from Kolhapur Institute of
Technology.
Anil P. Athane has completed M.E. Electronics Engineering
degree from Rajarambapu Institute of Technology. His
research area is image processing.

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