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A memory gradient algorithm for l2-l0 regularization with applications to image restoration
Abstract : In this paper, we consider a class of differentiable criteria for sparse image recovery problems. The regularization is applied to a linear transform of the target image. As special cases, it includes edge preserving measures or frame analysis potentials. As shown by our asymptotic results, the considered l2-l0 penalties may be employed to approximate solutions to l0 penalized optimization problems. One of the advantages of the approach is that it allows us to derive an efficient Majorize-Minimize Memory Gradient algorithm. The fast convergence properties of the proposed optimization algorithm are illustrated through image restoration examples.
Cited literature [15 references]
https://hal-upec-upem.archives-ouvertes.fr/hal-00687500
Contributor : Emilie Chouzenoux <>
Submitted on : Sunday, July 14, 2013 - 6:42:55 PM
Last modification on : Wednesday, February 26, 2020 - 7:06:06 PM
Document(s) archivé(s) le : Tuesday, October 15, 2013 - 2:20:09 AM
Contributor : Emilie Chouzenoux <>
Submitted on : Sunday, July 14, 2013 - 6:42:55 PM
Last modification on : Wednesday, February 26, 2020 - 7:06:06 PM
Document(s) archivé(s) le : Tuesday, October 15, 2013 - 2:20:09 AM
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Chouzenoux_ICIP_11.pdf
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