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Proceedings Paper

Mismatch and resolution in compressive imaging
Author(s): Albert Fannjiang; Wenjing Liao
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Paper Abstract

Highly coherent sensing matrices arise in discretization of continuum problems such as radar and medical imaging when the grid spacing is below the Rayleigh threshold as well as in using highly coherent, redundant dictionaries as sparsifying operators. Algorithms (BOMP, BLOOMP) based on techniques of band exclusion and local optimization are proposed to enhance Orthogonal Matching Pursuit (OMP) and deal with such coherent sensing matrices. BOMP and BLOOMP have provably performance guarantee of reconstructing sparse, widely separated objects independent of the redundancy and have a sparsity constraint and computational cost similar to OMP's. Numerical study demonstrates the effectiveness of BLOOMP for compressed sensing with highly coherent, redundant sensing matrices.

Paper Details

Date Published: 27 September 2011
PDF: 9 pages
Proc. SPIE 8138, Wavelets and Sparsity XIV, 81380Y (27 September 2011); doi: 10.1117/12.892434
Show Author Affiliations
Albert Fannjiang, Univ. of California, Davis (United States)
Wenjing Liao, Univ. of California, Davis (United States)

Published in SPIE Proceedings Vol. 8138:
Wavelets and Sparsity XIV
Manos Papadakis; Dimitri Van De Ville; Vivek K. Goyal, Editor(s)

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