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 / ConférenceExternal document link
https://hal.archives-ouvertes.fr/hal-00365607Date
2007Conference country
ITALYBook title
Scale Space and Variational Methods in Computer Vision First International Conference, SSVM 2007, Ischia, Italy, May 30 - June 2, 2007, ProceedingsBook author
Fiorella Sgallari, Almerico Murli, Nikos ParagiosPublisher
Springer
Published in
Berlin Heidelberg
ISBN
978-3-540-72822-1
Pages
80-91
Publication identifier
Metadata
Show full item recordAuthor(s)
Peyré, GabrielAbstract (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 problemRelated items
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