6 research outputs found
Mismatch and resolution in compressive imaging
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 {\em 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.Comment: Figure 5 revise
Mismatch and resolution in compressive imaging
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
Recommended from our members
Mismatch and resolution in compressive imaging
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 {\em 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