A greedy algorithm to extract sparsity degree for l1/l0-equivalence in a deterministic context
type: Dataset
description: 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.
landing page: EUSIPCO 2012
keyword: compressed-sensing
keyword: infoeu-reposemanticsarticle
keyword: infoinfo-tscomputer-science-cssignal-and-image-processing
keyword: journal-articles
keyword: spisignalengineering-sciences-physicssignal-and-image-processing
keyword: tomography
issued: 2026-05-10T23:56:52.845271
modified: 2026-05-10T23:56:52.845276
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

A greedy algorithm to extract sparsity degree for l1/l0-equivalence in a deterministic context
type: Distribution
format: HTML
access URL: https://hal.science/hal-00826821
issued: 2026-05-10T23:56:52.850953
modified: 2026-05-10T23:56:52.837799