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Iterative regularized mixed norm multichannel image restoration

[+] Author Affiliations
Min-Cheol Hong

Soongsil University, School of Electronic Engineering, Seoul, Korea

Tania Stathaki

Imperial College, Signal Processing and Digital System Section, United Kingdom

Aggelos K. Katsaggelos

Northwestern University, Department of Electrical and Computer Engineering, McCormick School of Engineering and Applied Science, Evanston, Illinois 60208 E-mail: mhong@e.ssu.ac.kr

J. Electron. Imaging. 14(1), 013004 (Mar. 1, 2005). doi:10.1117/1.1867452
History: Received Apr. 11, 2003; Revised Jan. 12, 2004; Accepted Apr. 2, 2004; Mar. 1, 2005; Online March 01, 2005
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We present a regularized mixed norm multichannel image restoration algorithm. The problem of multichannel restoration using both within- and between-channel deterministic information is considered. For each channel a functional that combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional is proposed. We introduce a mixed norm parameter that controls the relative contribution between the LMS and the LMF, and a regularization parameter that defines the degree of smoothness of the solution, both updated at each iteration according to the noise characteristics of each channel. The novelty of the proposed algorithm is that no knowledge of the noise distribution for each channel is required, and the parameters just mentioned are adjusted based on the partially restored image. © 2005 SPIE and IS&T.

© 2005 SPIE and IS&T

Citation

Min-Cheol Hong ; Tania Stathaki and Aggelos K. Katsaggelos
"Iterative regularized mixed norm multichannel image restoration", J. Electron. Imaging. 14(1), 013004 (Mar. 1, 2005). ; http://dx.doi.org/10.1117/1.1867452


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