Best Basis Compressed Sensing

Abstract : This paper proposes a best basis extension of compressed sensing recovery. Instead of regularizing the compressed sensing inverse problem with a sparsity prior in a fixed basis, our framework makes use of sparsity in a tree-structured dictionary of orthogonal bases. A new iterative thresholding algorithm performs both the recovery of the signal and the estimation of the best basis. The resulting reconstruction from compressive measurements optimizes the basis to the structure of the sensed signal. Adaptivity is crucial to capture the regularity of complex natural signals. Numerical experiments on sounds and geometrical images indeed show that this best basis search improves the recovery with respect to fixed sparsity priors.
Type de document :
Article dans une revue
IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2010, 58 (5), pp.2613-2622. 〈10.1109/TSP.2010.2042490〉
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-00365017
Contributeur : Gabriel Peyré <>
Soumis le : dimanche 10 janvier 2010 - 00:18:03
Dernière modification le : jeudi 11 janvier 2018 - 06:12:20
Document(s) archivé(s) le : mercredi 30 novembre 2016 - 10:30:50

Fichier

PeyreBestBasisCS.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Gabriel Peyré. Best Basis Compressed Sensing. IEEE Transactions on Signal Processing, Institute of Electrical and Electronics Engineers, 2010, 58 (5), pp.2613-2622. 〈10.1109/TSP.2010.2042490〉. 〈hal-00365017v3〉

Partager

Métriques

Consultations de la notice

2564

Téléchargements de fichiers

2887