DOSSAL, Charles; PEYRÉ, Gabriel; FADILI, Jalal M.

Metadatos

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Licencia de uso del documento

DOSSAL, Charles
Laboratoire Bordelais d'Analyse et Géométrie [LaBAG]

PEYRÉ, Gabriel
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]

FADILI, Jalal M.
Equipe Image - Laboratoire GREYC - UMR6072

Idioma

Communication dans un congrès

Este ítem está publicado en

Proceedeings of SPARS'09, Proceedeings of SPARS'09, SPARS'09, Signal Processing with Adaptive Sparse Structured Representations, 2009-04-06, Saint-Malo. 2009-04p. 5p.

Resumen en inglés

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.< Leer menos

Palabras clave en italiano

Compressed sensing

sparsity

L1 minimization

Proyecto ANR

Adaptivité pour la représentation des images naturelles et des textures - ANR-08-EMER-0009

Orígen

Importado de Hal

Centros de investigación

Institut de Mathématiques de Bordeaux (IMB) - UMR 5251