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Reseach Article

An Efficient Algorithm for Removal of Salt and Pepper Noise from Images

by Chetana Kankariya, Sudesh Gupta
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 118 - Number 18
Year of Publication: 2015
Authors: Chetana Kankariya, Sudesh Gupta

Chetana Kankariya, Sudesh Gupta . An Efficient Algorithm for Removal of Salt and Pepper Noise from Images. International Journal of Computer Applications. 118, 18 ( May 2015), 24-28. DOI=10.5120/20846-3502

@article{ 10.5120/20846-3502,
author = { Chetana Kankariya, Sudesh Gupta },
title = { An Efficient Algorithm for Removal of Salt and Pepper Noise from Images },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 118 },
number = { 18 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 24-28 },
numpages = {9},
url = { },
doi = { 10.5120/20846-3502 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T23:02:04.244712+05:30
%A Chetana Kankariya
%A Sudesh Gupta
%T An Efficient Algorithm for Removal of Salt and Pepper Noise from Images
%J International Journal of Computer Applications
%@ 0975-8887
%V 118
%N 18
%P 24-28
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

Noise deletion is a significant issue in the field of image processing. In this paper switching threshold algorithm for the elimination of high density salt and pepper noise is proposed. It is implemented in two steps; firstly switching threshold is applied to the whole image to detect the pixels as corrupted/uncorrupted. Secondly, new pixel value is anticipated only for the corrupted pixels. The algorithm use an adaptive length window having maximum size is of 7x7. The existing methods are Arithmetic Mean Filtering (AMF) technique, Geometric Mean Filtering (GMF) technique, Harmonic Mean Filtering (HMF) technique. The proposed algorithm automatically switches the window size as per the noise occurrence. Performance of algorithm is evaluated in terms of Mean square Error, Peak Signal to Noise Ratio, Image Enhancement Factor, and processing time and evaluated with other filtering techniques. Extensive simulation shows that proposed algorithm removes the noise even though noise level as high as 50% and produces better results than that of existing filtering techniques.

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Index Terms

Computer Science
Information Sciences


Impulse noise Adaptive median filter Peak signal to noise ratio (PSNR).