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Adaptive Wavelet Thresholding for Image Denoising and Compression (2000)
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| Venue: | IEEE TRANSACTIONS ON IMAGE PROCESSING |
| Citations: | 362 - 4 self |
BibTeX
@ARTICLE{Chang00adaptivewavelet,
author = {S. Grace Chang and Bin Yu and Martin Vetterli},
title = {Adaptive Wavelet Thresholding for Image Denoising and Compression},
journal = {IEEE TRANSACTIONS ON IMAGE PROCESSING},
year = {2000},
volume = {9},
number = {9},
pages = {1532--1546}
}
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Abstract
The first part of this paper proposes an adaptive, data-driven threshold for image denoising via wavelet soft-thresholding. The threshold is derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distribution (GGD) widely used in image processing applications. The proposed threshold is simple and closed-form, and it is adaptive to each subband because it depends on data-driven estimates of the parameters. Experimental results show that the proposed method, called BayesShrink, is typically within 5% of the MSE of the best soft-thresholding benchmark with the image assumed known. It also outperforms Donoho and Johnstone's SureShrink most of the time. The second part
