On the Role of Sparse and Redundant Representations in Image Processing

Abstract : Much of the progress made in image processing in the past decades can be attributed to better modeling of image content and a wise deployment of these models in relevant applications. This path of models spans from the simple l2-norm smoothness through robust, thus edge preserving, measures of smoothness (e.g. total variation), and until the very recent models that employ sparse and redundant representations. In this paper, we review the role of this recent model in image processing, its rationale, and models related to it. As it turns out, the field of image processing is one of the main beneficiaries from the recent progress made in the theory and practice of sparse and redundant representations. We discuss ways to employ these tools for various image-processing tasks and present several applications in which state-of-the-art results are obtained.
Type de document :
Article dans une revue
Proceedings of the IEEE, Institute of Electrical and Electronics Engineers, 2010, 98 (6), pp.972 -982. 〈10.1109/JPROC.2009.2037655〉
Liste complète des métadonnées

https://hal.inria.fr/inria-00568893
Contributeur : Jules Espiau de Lamaestre <>
Soumis le : mercredi 23 février 2011 - 17:57:46
Dernière modification le : mardi 26 décembre 2017 - 09:16:01

Lien texte intégral

Identifiants

Collections

Citation

Michael Elad, Mario A. T. Figueiredo, Yi Ma. On the Role of Sparse and Redundant Representations in Image Processing. Proceedings of the IEEE, Institute of Electrical and Electronics Engineers, 2010, 98 (6), pp.972 -982. 〈10.1109/JPROC.2009.2037655〉. 〈inria-00568893〉

Partager

Métriques

Consultations de la notice

112