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Title: Interscale image denoising with wavelet context modeling
Authors: Zhang, L 
Bao, P
Zhang, DD 
Issue Date: 2003
Source: 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing : April 6-10, 2003, Hong Kong, v. 6, p. 97-100
Abstract: This paper presents a wavelet-based linear minimum mean square-error estimation (LMMSE) scheme to exploit the strong wavelet interscale dependencies for image denoising. Using overcomplete wavelet expansion (OWE), we group the wavelet coefficients with the same spatial orientation at adjacent scales as a vector. The LMMSE algorithm is then applied to the vector variable. This scheme exploits the correlation information of wavelet scales to improve noise removal. To calculate the statistics of wavelet coefficients more adaptively, we classify them into different clusters by the context modeling technique, which yields a good local discrimination between edge structures and backgrounds. Experiments show that the proposed scheme outperforms some existing denoising methods. And a biorthogonal wavelet, which well characterizes the interscale dependencies, is found very suitable for the scheme.
Keywords: Correlation methods
Spurious signal noise
Statistical methods
Wavelet transforms
Publisher: IEEE
ISBN: 0-7803-7663-3
Rights: © 2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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