Iterative image deconvolution using overcomplete representations

Caroline Chaux 1 Patrick Louis Combettes 2 Jean-Christophe Pesquet 1 Valérie R. Wajs 2
1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 LJLL - Laboratoire Jacques-Louis Lions
Abstract : We consider the problem of deconvolving an image with a priori information on its representation in a frame. Our variational approach consists of minimizing the sum of a residual energy and a separable term penalizing each frame coef - cient individually. This penalization term may model various properties, in particular sparsity. A general iterative method is proposed and its convergence is established. The novelty of this work is to extend existing methods on two distinct fronts. First, a broad class of convex functions are allowed in the penalization term which, in turn, yields a new class of soft thresholding schemes. Second, while existing results are restricted to orthonormal bases, our algorithmic framework is applicable to much more general overcomplete representations. Numerical simulations are provided.
Type de document :
Communication dans un congrès
European Signal Processing Conference (EUSIPCO'06), Sep 2006, Florence, Italy. 10pp., 2006
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https://hal-upec-upem.archives-ouvertes.fr/hal-00621888
Contributeur : Caroline Chaux <>
Soumis le : mardi 9 juillet 2013 - 16:03:02
Dernière modification le : jeudi 21 mars 2019 - 14:30:56
Document(s) archivé(s) le : jeudi 10 octobre 2013 - 03:05:13

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Caroline Chaux, Patrick Louis Combettes, Jean-Christophe Pesquet, Valérie R. Wajs. Iterative image deconvolution using overcomplete representations. European Signal Processing Conference (EUSIPCO'06), Sep 2006, Florence, Italy. 10pp., 2006. 〈hal-00621888〉

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