Dual Sliding Statistics Switching Median Filter for the Removal of Low Level Random-Valued Impulse Noise

A new nonlinear filtering algorithm for effectively denoising images corrupted by the random-valued impulse noise, called dual sliding statistics switching median (DSSSM) filter is presented in this paper. The proposed DSSSM filter is made up of two subunits; i.e. Impulse noise detection and noise f...

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Main Authors:	Mohd Helmi, Suid, Mohd Falfazli, Mat Jusof, Mohd Ashraf, Ahmad
Format:	Article
Language:	English
Published:	KIEE 2018
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/23875/ http://umpir.ump.edu.my/id/eprint/23875/ http://umpir.ump.edu.my/id/eprint/23875/ http://umpir.ump.edu.my/id/eprint/23875/1/36-04-293_1383.pdf

id	ump-23875
recordtype	eprints
spelling	ump-238752019-01-21T02:34:11Z http://umpir.ump.edu.my/id/eprint/23875/ Dual Sliding Statistics Switching Median Filter for the Removal of Low Level Random-Valued Impulse Noise Mohd Helmi, Suid Mohd Falfazli, Mat Jusof Mohd Ashraf, Ahmad TK Electrical engineering. Electronics Nuclear engineering A new nonlinear filtering algorithm for effectively denoising images corrupted by the random-valued impulse noise, called dual sliding statistics switching median (DSSSM) filter is presented in this paper. The proposed DSSSM filter is made up of two subunits; i.e. Impulse noise detection and noise filtering. Initially, the impulse noise detection stage of DSSSM algorithm begins by processing the statistics of a localized detection window in sorted order and non-sorted order, simultaneously. Next, the median of absolute difference (MAD) obtained from both sorted statistics and non-sorted statistics will be further processed in order to classify any possible noise pixels. Subsequently, the filtering stage will replace the detected noise pixels with the estimated median value of the surrounding pixels. In addition, fuzzy based local information is used in the filtering stage to help the filter preserves the edges and details. Extensive simulations results conducted on gray scale images indicate that the DSSSM filter performs significantly better than a number of well-known impulse noise filters existing in literature in terms of noise suppression and detail preservation; with as much as 30% impulse noise corruption rate. Finally, this DSSSM filter is algorithmically simple and suitable to be implemented for electronic imaging products. KIEE 2018 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/23875/1/36-04-293_1383.pdf Mohd Helmi, Suid and Mohd Falfazli, Mat Jusof and Mohd Ashraf, Ahmad (2018) Dual Sliding Statistics Switching Median Filter for the Removal of Low Level Random-Valued Impulse Noise. Journal of Electrical Engineering & Technology, 13 (3). pp. 1383-1391. ISSN 1975-0102 http://home.jeet.or.kr/archives/view_articles.asp?seq=2083 https://doi.org/10.5370/JEET.2018.13.3.1383
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Mohd Helmi, Suid Mohd Falfazli, Mat Jusof Mohd Ashraf, Ahmad Dual Sliding Statistics Switching Median Filter for the Removal of Low Level Random-Valued Impulse Noise
description	A new nonlinear filtering algorithm for effectively denoising images corrupted by the random-valued impulse noise, called dual sliding statistics switching median (DSSSM) filter is presented in this paper. The proposed DSSSM filter is made up of two subunits; i.e. Impulse noise detection and noise filtering. Initially, the impulse noise detection stage of DSSSM algorithm begins by processing the statistics of a localized detection window in sorted order and non-sorted order, simultaneously. Next, the median of absolute difference (MAD) obtained from both sorted statistics and non-sorted statistics will be further processed in order to classify any possible noise pixels. Subsequently, the filtering stage will replace the detected noise pixels with the estimated median value of the surrounding pixels. In addition, fuzzy based local information is used in the filtering stage to help the filter preserves the edges and details. Extensive simulations results conducted on gray scale images indicate that the DSSSM filter performs significantly better than a number of well-known impulse noise filters existing in literature in terms of noise suppression and detail preservation; with as much as 30% impulse noise corruption rate. Finally, this DSSSM filter is algorithmically simple and suitable to be implemented for electronic imaging products.
format	Article
author	Mohd Helmi, Suid Mohd Falfazli, Mat Jusof Mohd Ashraf, Ahmad
author_facet	Mohd Helmi, Suid Mohd Falfazli, Mat Jusof Mohd Ashraf, Ahmad
author_sort	Mohd Helmi, Suid
title	Dual Sliding Statistics Switching Median Filter for the Removal of Low Level Random-Valued Impulse Noise
title_short	Dual Sliding Statistics Switching Median Filter for the Removal of Low Level Random-Valued Impulse Noise
title_full	Dual Sliding Statistics Switching Median Filter for the Removal of Low Level Random-Valued Impulse Noise
title_fullStr	Dual Sliding Statistics Switching Median Filter for the Removal of Low Level Random-Valued Impulse Noise
title_full_unstemmed	Dual Sliding Statistics Switching Median Filter for the Removal of Low Level Random-Valued Impulse Noise
title_sort	dual sliding statistics switching median filter for the removal of low level random-valued impulse noise
publisher	KIEE
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/23875/ http://umpir.ump.edu.my/id/eprint/23875/ http://umpir.ump.edu.my/id/eprint/23875/ http://umpir.ump.edu.my/id/eprint/23875/1/36-04-293_1383.pdf
first_indexed	2023-09-18T22:35:57Z
last_indexed	2023-09-18T22:35:57Z
_version_	1777416596306788352

Dual Sliding Statistics Switching Median Filter for the Removal of Low Level Random-Valued Impulse Noise

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