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Proceedings Paper

Globally optimal smoothing functional for multichannel image restoration
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Paper Abstract

It is expected that a globally optimal restored multichannel image should be superior to a suboptimally restored image without the use of cross-channel information. In this paper, a regularized multichannel image restoration approach is proposed, which is based on the minimum multichannel regularized noise power criterion. Furthermore, no prior knowledge about the variance of the noise at each channel and a bound on the high frequency energy of the image are assumed, but this information is estimated based on the partially restored result at each step. The multichannel smoothing functional to be minimized is formulated to have a global minimizer with the proper choice of the multichannel regularization functionals. With this algorithm, the regularization functional for each channel is determined by incorporating not only within-channel information but also cross-channel information. It is also shown that the proposed multichannel smoothing functional is convex, and therefore, has a global minimizer. The proposed multichannel algorithm not only does not depend on initial conditions but is also shown to be much more computationally efficient than existing algorithms.

Paper Details

Date Published: 16 September 1994
PDF: 12 pages
Proc. SPIE 2308, Visual Communications and Image Processing '94, (16 September 1994); doi: 10.1117/12.185965
Show Author Affiliations
Moon Gi Kang, Northwestern Univ. (United States)
Aggelos K. Katsaggelos, Northwestern Univ. (United States)

Published in SPIE Proceedings Vol. 2308:
Visual Communications and Image Processing '94
Aggelos K. Katsaggelos, Editor(s)

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