A Numerical Exploration of Compressed Sampling Recovery

Abstract : This paper explores numerically the efficiency of $\lun$ minimization for the recovery of sparse signals from compressed sampling measurements in the noiseless case. Inspired by topological criteria for $\lun$-identifiability, a greedy algorithm computes sparse vectors that are difficult to recover by $\ell_1$-minimization. We evaluate numerically the theoretical analysis without resorting to Monte-Carlo sampling, which tends to avoid worst case scenarios. This allows one to challenge sparse recovery conditions based on polytope projection and on the restricted isometry property.
Type de document :
Communication dans un congrès
SPARS'09, Signal Processing with Adaptive Sparse Structured Representations, Apr 2009, Saint-Malo, France. 5p., 2009
Liste complète des métadonnées

Littérature citée [5 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-00365028
Contributeur : Gabriel Peyré <>
Soumis le : lundi 2 mars 2009 - 08:48:37
Dernière modification le : mardi 5 juin 2018 - 10:14:42
Document(s) archivé(s) le : vendredi 12 octobre 2012 - 12:40:58

Fichier

DossalPeyreFadili-SPARS09.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00365028, version 1

Citation

Charles Dossal, Gabriel Peyré, Jalal M. Fadili. A Numerical Exploration of Compressed Sampling Recovery. SPARS'09, Signal Processing with Adaptive Sparse Structured Representations, Apr 2009, Saint-Malo, France. 5p., 2009. 〈hal-00365028〉

Partager

Métriques

Consultations de la notice

671

Téléchargements de fichiers

177