Wavelet image denoising using localized thresholding operators

Authors:
M. Ghazel;G. H. Freeman;E. R. Vrscay;R. K. Ward
Affiliations:
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC;Department of Electrical and Computer Engineering;Department of Applied Mathemarics, University of Waterloo, Waterloo, ON;Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC
Venue:
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
Year:
2005

Citing 2
Cited 0

De-noising by soft-thresholding

IEEE Transactions on Information Theory
Spatially adaptive wavelet thresholding with context modeling for image denoising

IEEE Transactions on Image Processing

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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.