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Iterative image deconvolution using overcomplete representations
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.
https://hal-upec-upem.archives-ouvertes.fr/hal-00621888
Contributor : Caroline Chaux <>
Submitted on : Tuesday, July 9, 2013 - 4:03:02 PM
Last modification on : Friday, March 27, 2020 - 3:53:00 AM
Document(s) archivé(s) le : Thursday, October 10, 2013 - 3:05:13 AM
Contributor : Caroline Chaux <>
Submitted on : Tuesday, July 9, 2013 - 4:03:02 PM
Last modification on : Friday, March 27, 2020 - 3:53:00 AM
Document(s) archivé(s) le : Thursday, October 10, 2013 - 3:05:13 AM
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eusipco06Chaux.pdf
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