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Neural Computing and Applications

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01-03-2012 | Original Article

Adaptive threshold selection for impulsive noise detection in images using coefficient of variance

Authors: Subrajeet Mohapatra, Pankaj Kumar Sa, Banshidhar Majhi

Published in: Neural Computing and Applications | Issue 2/2012

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Abstract

This paper proposes an adaptive threshold selection strategy to detect impulsive noise in images. The proposed method utilizes a simple neural network with statistical characteristics of noisy images. The method is adaptive in the sense that the threshold obtained is adaptable to different type of images and noise conditions. The network tuned for one image works for other images as well at different noise conditions. Comparative analysis with other standard techniques reveals that the proposed scheme outperforms its counterparts in terms of noise suppression.

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Title: Adaptive threshold selection for impulsive noise detection in images using coefficient of variance
Authors: Subrajeet Mohapatra
Pankaj Kumar Sa
Banshidhar Majhi
Publication date: 01-03-2012
Publisher: Springer-Verlag
Published in: Neural Computing and Applications / Issue 2/2012
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0583-9

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