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Best Basis Compressed Sensing

Peyré, Gabriel (2007), Best Basis Compressed Sensing, in Fiorella Sgallari, Almerico Murli, Nikos Paragios, Scale Space and Variational Methods in Computer Vision First International Conference, SSVM 2007, Ischia, Italy, May 30 - June 2, 2007, Proceedings, Springer : Berlin Heidelberg, p. 80-91. 10.1007/978-3-540-72823-8_8

Type
Communication / Conférence
External document link
https://hal.archives-ouvertes.fr/hal-00365607
Date
2007
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
Book author
Fiorella Sgallari, Almerico Murli, Nikos Paragios
Publisher
Springer
Published in
Berlin Heidelberg
ISBN
978-3-540-72822-1
Pages
80-91
Publication identifier
10.1007/978-3-540-72823-8_8
Metadata
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Author(s)
Peyré, Gabriel
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.
Subjects / Keywords
adaptivity; Compressed sensing; best basis; inverse problem

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