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Proceedings Paper

Iterative regularized mixed-norm image restoration algorithm
Author(s): Min-Cheol Hong; Tania Stathaki; Aggelos K. Katsaggelos
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Paper Abstract

This paper introduces a regularized mixed-norm image restoration algorithm. A functional which combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional is proposed.A function of the kurtosis is used to determine the relative importance between the LMS and the LMF functionals, and a function of the previous two functionals an the smoothing functional is utilized for determining the regularization parameter. The two parameters are chosen in such a way that the proposed functional is convex, so that a local minimizer becomes a global minimizer. The novelty of the proposed algorithm is than no knowledge of the noise distribution is required, and the relative contribution of the LMS, the LMF and the smoothing functional is adjusted based on the partially restored image.

Paper Details

Date Published: 9 January 1998
PDF: 12 pages
Proc. SPIE 3309, Visual Communications and Image Processing '98, (9 January 1998); doi: 10.1117/12.298374
Show Author Affiliations
Min-Cheol Hong, Northwestern Univ. (South Korea)
Tania Stathaki, Imperial College of Science, Technology,and Medicine (United Kingdom)
Aggelos K. Katsaggelos, Northwestern Univ. (United States)


Published in SPIE Proceedings Vol. 3309:
Visual Communications and Image Processing '98
Sarah A. Rajala; Majid Rabbani, Editor(s)

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