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| HAL : hal-00542016, version 1 |
| Fiche détaillée |  Récupérer au format |
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| Sampta'11, Singapour (2011) |
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| Learning Analysis Sparsity Priors |
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| Gabriel Peyré 1Jalal Fadili 2 |
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| (2011) |
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| This paper introduces a novel approach to learn a dictionary in a sparsity-promoting analysis-type prior. The dictionary is opti- mized in order to optimally restore a set of exemplars from their degraded noisy versions. Towards this goal, we cast our prob- lem as a bilevel programming problem for which we propose a gradient descent algorithm to reach a stationary point that might be a local minimizer. When the dictionary analysis operator specializes to a convolution, our method turns out to be a way of learning generalized total variation-type prior. Applications to 1-D signal denoising are reported and potential applicability and extensions are discusses. |
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| 1 : | CEntre de REcherches en MAthématiques de la DEcision (CEREMADE) |
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CNRS : UMR7534 – Université Paris IX - Paris Dauphine |
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| 2 : | Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen (GREYC) |
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CNRS : UMR6072 – Université de Caen – Ecole Nationale Supérieure d'Ingénieurs de Caen |
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| Domaine | : | Informatique/Traitement du signal et de l'image Sciences de l'ingénieur/Traitement du signal et de l'image |
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| Dictionary learning – analysis prior – total variation – denoising |
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| Liste des fichiers attachés à ce document : | |||||
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| hal-00542016, version 1 | |
| http://hal.archives-ouvertes.fr/hal-00542016 | |
| oai:hal.archives-ouvertes.fr:hal-00542016 | |
| Contributeur : Gabriel Peyré | |
| Soumis le : Mercredi 1 Décembre 2010, 16:32:17 | |
| Dernière modification le : Mercredi 23 Mai 2012, 21:52:42 | |
