Improved Adaptive Impulsive Noise Suppression

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/500

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DC Field	Value	Language
dc.contributor.author	Sa, Pankaj K	-
dc.contributor.author	Majhi, B	-
dc.contributor.author	Panda, G	-
dc.date.accessioned	2007-09-25T04:40:19Z	-
dc.date.available	2007-09-25T04:40:19Z	-
dc.date.issued	2007	-
dc.identifier.citation	IEEE International Fuzzy Systems Conference, 23-26 July 2007 P 1-4	en
dc.identifier.uri	http://dx.doi.org/10.1109/FUZZY.2007.4295380	-
dc.identifier.uri	http://hdl.handle.net/2080/500	-
dc.description	Copyright for this article belongs to IEEE	en
dc.description.abstract	In this work an improved scheme for eliminating impulsive noise of varying strengths from corrupted images is proposed. A neural network is employed to classify the corrupted and non-corrupted pixels. Filtering is only carried out on corrupted pixels keeping the non-corrupted ones intact. Emphasis has been put on selection of relevant input and training patterns. With appropriate choice of patterns the assiduous task of training has become effortless as well as the noise detection become reliable. Comparative analysis with competent schemes on standard images at different noise conditions shows that the proposed scheme outperforms its counterparts.	en
dc.format.extent	1598861 bytes	-
dc.format.mimetype	application/pdf	-
dc.language.iso	en	-
dc.publisher	IEEE	en
dc.title	Improved Adaptive Impulsive Noise Suppression	en
dc.type	Article	en
Appears in Collections:	Conference Papers

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