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Proceedings Paper

Multichannel image identification and restoration based on the EM algorithm and cross-validation
Author(s): Aggelos K. Katsaggelos; Nikolas P. Galatsanos; Kuen-Tsair Lay; Wenwu Zhu
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Paper Abstract

In this paper we address some of the main shortcomings of multi-channel (MC) linear restoration filters. The problem of restoring a MC image and simultaneously estimating the MC power spectrum of the image and the noise, required by linear minimum mean squared error (LMMSE) filters is investigated, using the expectation-maximization (EM) algorithm. Second, the problem of estimating, the regularization parameters and operator, required by regularized least-squares (RLS) MC restoration filters is investigated using the cross-validation (CV) function. Furthermore, a novel representation of MC signal processing is introduced. This notation leads to a more natural extension of single-channel (SC) signal processing algorithms to the MC case and yields a new class of matrices which we call semi-block- circulant (SBC) matrices. The properties of these matrices are examined and a family of new efficient algorithms is developed for the computation of the MC EM and CV functions.

Paper Details

Date Published: 29 December 1992
PDF: 12 pages
Proc. SPIE 1767, Inverse Problems in Scattering and Imaging, (29 December 1992); doi: 10.1117/12.139009
Show Author Affiliations
Aggelos K. Katsaggelos, Northwestern Univ. (United States)
Nikolas P. Galatsanos, Illinois Institute of Technology (United States)
Kuen-Tsair Lay, National Taiwan Institute of Technology (Taiwan)
Wenwu Zhu, Illinois Institute of Technology (United States)

Published in SPIE Proceedings Vol. 1767:
Inverse Problems in Scattering and Imaging
Michael A. Fiddy, Editor(s)

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