A Numerical Exploration of Compressed Sampling Recovery
Language
en
Communication dans un congrès
This item was published in
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.
English 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, ...Read more >
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.Read less <
Italian Keywords
Compressed sensing
sparsity
L1 minimization
ANR Project
Adaptivité pour la représentation des images naturelles et des textures - ANR-08-EMER-0009
Origin
Hal imported