On iterative compressed sensing reconstruction of sparse non-negative vectors

  • Authors:
  • Vida Ravanmehr;Ludovic Danjean;David Declercq;Bane Vasić

  • Affiliations:
  • University of Arizona, Tucson, AZ;ETIS, ENSEA/Univ. Cergy-Pontoise/CNRS, Cergy-Pontoise, France, and University of Arizona, Tucson, AZ;ETIS, ENSEA/Univ. Cergy-Pontoise/CNRS, Cergy-Pontoise, France;University of Arizona, Tucson, AZ

  • Venue:
  • Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
  • Year:
  • 2011

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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.