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Proceedings Paper

Multichannel image deconvolution by total variation regularization
Author(s): Tony F. Chan; Chiu-Kwong T. Wong
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Paper Abstract

The total variational (TV) regularization method was first proposed for gray scale images and was extended for vector valued images. In this work, we apply the TV regularization method to solve the multichannel image deconvolution problem. The motivation for regularizing with the TV norm is that it is extremely effective for recovering edges of images. In this paper, a fast iterative method is developed to solve the deconvolution problem. Our method involves solving linear systems and the conjugate gradient method is applied in which Fourier transform type preconditioners are used to speed up the convergence rate. Numerical experiments demonstrate the effectiveness of the TV regularization method. In this paper, we present some preliminary results on multichannel blind deconvolution with TV regularization.

Paper Details

Date Published: 24 October 1997
PDF: 9 pages
Proc. SPIE 3162, Advanced Signal Processing: Algorithms, Architectures, and Implementations VII, (24 October 1997); doi: 10.1117/12.284189
Show Author Affiliations
Tony F. Chan, Univ. of California/Los Angeles (United States)
Chiu-Kwong T. Wong, Univ. of California/Los Angeles (United States)


Published in SPIE Proceedings Vol. 3162:
Advanced Signal Processing: Algorithms, Architectures, and Implementations VII
Franklin T. Luk, Editor(s)

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