Random Sensing of Geometric Images

Peyré, Gabriel

Peyré, Gabriel (2006), Random Sensing of Geometric Images, NeuroComp'06, Pont-à-Mousson, FRANCE

Type

Communication / Conférence

Date

2006

Titre du colloque

NeuroComp'06

Ville du colloque

Pont-à-Mousson

Pays du colloque

FRANCE

Métadonnées

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Auteur(s)

Peyré, Gabriel

Résumé (EN)

This paper proposes an extension of compressed sensing that allows to express the sparsity prior in a dictionary of bases. This enables the use of the universal sampling strategy of compressed sensing together with an adaptive recovery process that adapts the basis to the structure of the sensed signal. A fast greedy scheme is used during reconstruction to estimate the best basis using an iterative refinement. Numerical experiments on geometrical images show that adaptivity is indeed crucial to capture the structures of complex natural signals.

Mots-clés

bandlets; Compressed sensing; best basis