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Best Basis Compressed Sensing

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Date
2007
Link to item file
https://hal.archives-ouvertes.fr/hal-00365607
Dewey
Probabilités et mathématiques appliquées
Sujet
adaptivity; Compressed sensing; best basis; inverse problem
DOI
http://dx.doi.org/10.1007/978-3-540-72823-8_8
Conference country
ITALY
Book title
Scale Space and Variational Methods in Computer Vision First International Conference, SSVM 2007, Ischia, Italy, May 30 - June 2, 2007, Proceedings
Author
Fiorella Sgallari, Almerico Murli, Nikos Paragios
Publisher
Springer
Publisher city
Berlin Heidelberg
Year
2007
ISBN
978-3-540-72822-1
Book URL
10.1007/978-3-540-72823-8
URI
https://basepub.dauphine.fr/handle/123456789/452
Collections
  • CEREMADE : Publications
Metadata
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Author
Peyré, Gabriel
Type
Communication / Conférence
Item number of pages
80-91
Abstract (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 random 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 sounds and geometrical images show that adaptivity is indeed crucial to capture the structures of complex natural signals.

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