A greedy algorithm to extract sparsity degree for l1/l0-equivalence in a deterministic context
Language
en
Communication dans un congrès
This item was published in
EUSIPCO 2012, EUSIPCO 2012, European Signal Processing Conference (EUSIPCO), 2012-08-27, Bucharest. 2012-08-27p. x+5
English Abstract
This paper investigates the problem of designing a deterministic system matrix, that is measurement matrix, for sparse recovery. An efficient greedy algorithm is proposed in order to extract the class of sparse signal/image ...Read more >
This paper investigates the problem of designing a deterministic system matrix, that is measurement matrix, for sparse recovery. An efficient greedy algorithm is proposed in order to extract the class of sparse signal/image which cannot be reconstructed by $\ell_1$-minimization for a fixed system matrix. Based on the polytope theory, the algorithm provides a geometric interpretation of the recovery condition considering the seminal work by Donoho. The paper presents an additional condition, extending the Fuchs/Tropp results, in order to deal with noisy measurements. Simulations are conducted for tomography-like imaging system in which the design of the system matrix is a difficult task consisting of the selection of the number of views according to the sparsity degree.Read less <
English Keywords
Compressed sensing
tomography
polytope theory
Origin
Hal imported