Wavelet image denoising using localized thresholding operators

TitleWavelet image denoising using localized thresholding operators
Publication TypeJournal Article
Year of Publication2005
AuthorsGhazel, M., G. H. Freeman, E. R. Vrscay, and R. K. Ward
Secondary AuthorsKamel, M., and A. Campilho
JournalImage Analysis and Recognition
Volume3656
Pagination149–158
ISSN0302-9743
Abstract

In this paper, a localized wavelet thresholding strategy which adopts context-based thresholding operators is proposed. Traditional wavelet thresholding methods, such as VisuShrink, LevelShrink and BayesShrink, apply the conventional hard and soft thresholding operators and only differ in the selection of the threshold. The conventional soft and hard thresholding operators are point operators in the sense that only the value of the processed wavelet coefficient is taken into consideration before thresholding it. In this work, it will be shown that the performance of some of the standard wavelet thresholding methods can be improved by applying a localized, context-based, thresholding strategy instead of the conventional thresholding operators.

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