 |
|
|
|
| HAL : hal-00365028, version 1 |
| Fiche détaillée |  Récupérer au format |
|
|
| SPARS'09, Signal Processing with Adaptive Sparse Structured Representations, Saint-Malo : France (2009) |
|
|
|
|
| A Numerical Exploration of Compressed Sampling Recovery |
|
|
| Charles Dossal 1Gabriel Peyré 2 |
|
|
| (04/2009) |
|
|
|
| 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. |
|
|
|
|
|
|
|
|
|
|
|
| 1 : | Laboratoire Bordelais d'Analyse et Géométrie (LaBAG) |
|
|
|
CNRS : UMR5467 – Université Sciences et Technologies - Bordeaux I |
|
|
| 2 : | CEntre de REcherches en MAthématiques de la DEcision (CEREMADE) |
|
|
|
CNRS : UMR7534 – Université Paris IX - Paris Dauphine |
|
|
| 3 : | Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen (GREYC) |
|
|
|
CNRS : UMR6072 – Université de Caen – Ecole Nationale Supérieure d'Ingénieurs de Caen |
|
|
|
|
|
|
|
|
|
| Domaine | : | Mathématiques/Théorie de l'information et codage Informatique/Théorie de l'information et codage |
|
|
| Compressed sensing – sparsity – L1 minimization |
|
|
|
| Liste des fichiers attachés à ce document : | |||||
|
|||||
|
|
|
| hal-00365028, version 1 | |
| http://hal.archives-ouvertes.fr/hal-00365028 | |
| oai:hal.archives-ouvertes.fr:hal-00365028 | |
| Contributeur : Gabriel Peyré | |
| Soumis le : Lundi 2 Mars 2009, 08:48:37 | |
| Dernière modification le : Mardi 3 Mars 2009, 11:50:00 | |
