Best Basis Compressed Sensing

Peyré, Gabriel

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

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