DocumentCode :
1373389
Title :
Adaptive wavelet thresholding for image denoising and compression
Author :
Chang, S. Grace ; Yu, Bin ; Vetterli, Martin
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA
Volume :
9
Issue :
9
fYear :
2000
fDate :
9/1/2000 12:00:00 AM
Firstpage :
1532
Lastpage :
1546
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 SureShrink (Donoho and Johnstone 1994, 1995; Donoho 1995) most of the time. The second part of the paper attempts to further validate claims that lossy compression can be used for denoising. The BayesShrink threshold can aid in the parameter selection of a coder designed with the intention of denoising, and thus achieving simultaneous denoising and compression. Specifically, the zero-zone in the quantization step of compression is analogous to the threshold value in the thresholding function. The remaining coder design parameters are chosen based on a criterion derived from Rissanen´s minimum description length (MDL) principle. Experiments show that this compression method does indeed remove noise significantly, especially for large noise power. However, it introduces quantization noise and should be used only if bitrate were an additional concern to denoising
Keywords :
Bayes methods; Gaussian distribution; adaptive signal processing; data compression; image coding; image restoration; wavelet transforms; BayesShrink; Bayesian framework; MDL principle; Rissanen minimum description length principle; adaptive data-driven threshold; adaptive wavelet thresholding; compression; generalized Gaussian distribution; image denoising; lossy compression; parameter selection; quantization noise; quantization step; wavelet coefficients; wavelet soft-thresholding; zero-zone; Bayesian methods; Bit rate; Gaussian distribution; Image coding; Image denoising; Image processing; Noise reduction; Parameter estimation; Quantization; Wavelet coefficients;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/83.862633
Filename :
862633
Link To Document :
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