On iterative compressed sensing reconstruction of sparse non-negative vectors

Vida Ravanmehr, Ludovic Danjean, David Declercq, Bane Vasić

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider the iterative reconstruction of the Compressed Sensing (CS) problem over reals. The iterative reconstruction allows interpretation as a channel-coding problem, and it guarantees perfect reconstruction for properly chosen measurement matrices and sufficiently sparse error vectors. In this paper, we give a summary on reconstruction algorithms for compressed sensing and examine how the iterative reconstruction performs on quasi-cyclic low-density parity check (QC-LDPC) measurement matrices.

Original languageEnglish (US)
Title of host publicationProceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL'11
DOIs
StatePublished - Dec 1 2011
Event4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL'11 - Barcelona, Spain
Duration: Oct 26 2011Oct 29 2011

Publication series

NameACM International Conference Proceeding Series

Other

Other4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL'11
CountrySpain
CityBarcelona
Period10/26/1110/29/11

Keywords

  • bipartite graphs
  • compressed sensing
  • low-density parity check codes
  • message-passing algorithm

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Ravanmehr, V., Danjean, L., Declercq, D., & Vasić, B. (2011). On iterative compressed sensing reconstruction of sparse non-negative vectors. In Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL'11 (ACM International Conference Proceeding Series). https://doi.org/10.1145/2093698.2093844