Cosparse Analysis Modeling

Abstract : In the past decade there has been a great interest in a synthesis-based model for signals, based on sparse and redundant representations. Such a model assumes that the signal of interest can be composed as a linear combination of few columns from a given matrix (the dictionary). An alternative analysis-based model can be envisioned, where an analysis operator multiplies the signal, leading to a cosparse outcome. In this paper, we consider this analysis model, in the context of a generic missing data problem (e.g., compressed sensing, inpainting, source separation, etc.). Our work proposes a uniqueness result for the solution of this problem, based on properties of the analysis operator and the measurement matrix. This paper also considers two pursuit algorithms for solving the missing data problem, an L1-based and a new greedy method. Our simulations demonstrate the appeal of the analysis model, and the success of the pursuit techniques presented. In this work, we consider what we call cosparse analysis model, in the context of a generic missing data problem (e.g., compressed sensing, inpainting, source separation, etc.). Our work proposes a uniqueness result for the solution of this problem and considers algorithms to solving it.
Type de document :
Communication dans un congrès
The 9th International Conference on Sampling Theory and Applications, May 2011, Singapore, Singapore. 2011
Liste complète des métadonnées

Littérature citée [8 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00591779
Contributeur : Sangnam Nam <>
Soumis le : vendredi 16 septembre 2011 - 11:10:34
Dernière modification le : mercredi 16 mai 2018 - 11:23:03
Document(s) archivé(s) le : mardi 13 novembre 2012 - 10:52:10

Fichier

P0178.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00591779, version 1

Citation

Sangnam Nam, Mike E. Davies, Michael Elad, Rémi Gribonval. Cosparse Analysis Modeling. The 9th International Conference on Sampling Theory and Applications, May 2011, Singapore, Singapore. 2011. 〈inria-00591779〉

Partager

Métriques

Consultations de la notice

965

Téléchargements de fichiers

1157