A modified reconstruction algorithm for compressed sensing with least square residual

L 1 -norm based solver has been successfully used for sparse signal reconstruction in compressed sensing. In the paper, we propose a modified method to boost the decoding performance with least-square residual for L 1 algorithms. A further performance improvement is obtained by applying iteratively...

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Published in2015 International Conference on Communications and Signal Processing (ICCSP) pp. 0168 - 0171
Main Authors Shengqi Liu, Ronghui Zhan, Qinglin Zhai, Jiemin Hu, Jun Zhang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2015
Subjects
Compressed sensing
Current measurement
Indexes
iteratively reweighted L 1 minimization
L 1 -norm
least square residual
Noise level
Signal processing algorithms
Target recognition
Time measurement
Yttrium
Online AccessGet full text
DOI10.1109/ICCSP.2015.7322802

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Abstract L 1 -norm based solver has been successfully used for sparse signal reconstruction in compressed sensing. In the paper, we propose a modified method to boost the decoding performance with least-square residual for L 1 algorithms. A further performance improvement is obtained by applying iteratively reweighted L 1 minimization for sparsity pattern detection. Numerical experiments show that both the proposed methods lead to a better sparsity-measurement tradeoff than their benchmark algorithms.
AbstractList L 1 -norm based solver has been successfully used for sparse signal reconstruction in compressed sensing. In the paper, we propose a modified method to boost the decoding performance with least-square residual for L 1 algorithms. A further performance improvement is obtained by applying iteratively reweighted L 1 minimization for sparsity pattern detection. Numerical experiments show that both the proposed methods lead to a better sparsity-measurement tradeoff than their benchmark algorithms.
Author Shengqi Liu
Ronghui Zhan
Jun Zhang
Jiemin Hu
Qinglin Zhai
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  surname: Jun Zhang
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  organization: Sci. & Technol. on Autom. Target Recognition Lab., Nat. Univ. of Defense Technol., Changsha, China
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Snippet L 1 -norm based solver has been successfully used for sparse signal reconstruction in compressed sensing. In the paper, we propose a modified method to boost...
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StartPage 0168
SubjectTerms Compressed sensing
Current measurement
Indexes
iteratively reweighted L 1 minimization
L 1 -norm
least square residual
Noise level
Signal processing algorithms
Target recognition
Time measurement
Yttrium
Title A modified reconstruction algorithm for compressed sensing with least square residual
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