A Numerical Exploration of Compressed Sampling Recovery
Langue
en
Communication dans un congrès
Ce document a été publié dans
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.
Résumé en anglais
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, ...Lire la suite >
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.< Réduire
Mots clés en italien
Compressed sensing
sparsity
L1 minimization
Project ANR
Adaptivité pour la représentation des images naturelles et des textures - ANR-08-EMER-0009
Origine
Importé de halUnités de recherche