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| HAL : hal-00365017, version 3 |
| DOI : 10.1109/TSP.2010.2042490 |
| Fiche détaillée |  Récupérer au format |
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| IEEE Transactions on Signal Processing 58, 5 (2010) 2613-2622 |
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| Versions disponibles : | v1 (02-03-2009) | v2 (15-03-2009) | v3 (10-01-2010) |
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| Best Basis Compressed Sensing |
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| Gabriel Peyré 1 |
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| (2010) |
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| 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. |
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| 1 : | CEntre de REcherches en MAthématiques de la DEcision (CEREMADE) |
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CNRS : UMR7534 – Université Paris IX - Paris Dauphine |
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| Domaine | : | Informatique/Traitement du signal et de l'image Sciences de l'ingénieur/Traitement du signal et de l'image |
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| Compressed sensing – best basis – wavelet packets – cosine packets – bandlets. |
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| hal-00365017, version 3 | |
| http://hal.archives-ouvertes.fr/hal-00365017 | |
| oai:hal.archives-ouvertes.fr:hal-00365017 | |
| Contributeur : Gabriel Peyré | |
| Soumis le : Dimanche 10 Janvier 2010, 00:18:03 | |
| Dernière modification le : Vendredi 16 Avril 2010, 01:47:11 | |
