Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33090
Title: Context-based bias removal of statistical models of wavelet coefficients for image denoising
Authors: Dong, W
Wu, X
Shi, G
Zhang, L 
Keywords: Bayesian shrinkage
Context modeling
Estimation bias
Image denoising
Issue Date: 2009
Publisher: IEEE
Source: 2009 16th IEEE International Conference on Image Processing (ICIP), 7-10 November 2009, Cairo, p. 3841-3844 How to cite?
Abstract: Existing wavelet-based image denoising techniques all assume a probability model of wavelet coefficients that has zero mean, such as zero-mean Laplacian, Gaussian, or generalized Gaussian distributions. While such a zero-mean probability model fits a wavelet subband well, in areas of edges and textures the distribution of wavelet coefficients exhibits a significant bias. We propose a context modeling technique to estimate the expectation of each wavelet coefficient conditioned on the local signal structure. The estimated expectation is then used to shift the probability model of wavelet coefficient back to zero. This bias removal technique can significantly improve the performance of existing wavelet-based image denoisers.
URI: http://hdl.handle.net/10397/33090
ISBN: 978-1-4244-5653-6
978-1-4244-5655-0 (E-ISBN)
ISSN: 1522-4880
DOI: 10.1109/ICIP.2009.5414255
Appears in Collections:Conference Paper

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