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
TypeCommunication / Conférence
External document linkhttps://hal.archives-ouvertes.fr/hal-00365607
Book titleScale Space and Variational Methods in Computer Vision First International Conference, SSVM 2007, Ischia, Italy, May 30 - June 2, 2007, Proceedings
Book authorFiorella Sgallari, Almerico Murli, Nikos Paragios
MetadataShow full item record
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 / Keywordsadaptivity; Compressed sensing; best basis; inverse problem
Showing items related by title and author.