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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 2/2017

31-03-2015 | Original Article

Reconstructing images corrupted by noise based on D–S evidence theory

Authors: Ye Zhao, Ju-sheng Mi, Xin Liu, Xiao-yun Sun

Published in: International Journal of Machine Learning and Cybernetics | Issue 2/2017

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Abstract

In this paper, a new algorithm of noise reduction for image based on evidence theory is proposed. The values of all pixels are restricted in interval [0, 1], and set of data in each column is a term of mass function, which can be calculated by D–S composition rule. Judging noise can be achieved by comparing with the value of pixel in middle and of the current one. The noise will be removed by substituting the current value with value computed. An improved accelerated algorithm is also presented by sample window of 2 × 2. As a measure of conflict K with greater value shows that there would be noises within the current sample window. At last, Experiment image “Lena” with additive noise shows as a test sample, that better result can be achieved with the algorithm.
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Metadata
Title
Reconstructing images corrupted by noise based on D–S evidence theory
Authors
Ye Zhao
Ju-sheng Mi
Xin Liu
Xiao-yun Sun
Publication date
31-03-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 2/2017
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0353-6

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