IEICE TRANSACTIONS on Fundamentals
Improvement of Semi-Random Measurement Matrix for Compressed Sensing
Summary :
In compressed sensing, the design of the measurement matrix is a key work. In order to achieve a more precise reconstruction result, the columns of the measurement matrix should have better orthogonality or linear incoherence. A random matrix, like a Gaussian random matrix (GRM), is commonly adopted as the measurement matrix currently. However, the columns of the random matrix are only statistically-orthogonal. By substituting an orthogonal basis into the random matrix to construct a semi-random measurement matrix and by optimizing the mutual coherence between dictionary columns to approach a theoretical lower bound, the linear incoherence of the measurement matrix can be greatly improved. With this optimization measurement matrix, the signal can be reconstructed from its measures more precisely.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E97-A No.6 pp.1426-1429
- Publication Date
- 2014/06/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E97.A.1426
- Type of Manuscript
- LETTER
- Category
- Digital Signal Processing
Authors
Wentao LV
Shanghai Jiao Tong University
Junfeng WANG
Shanghai Jiao Tong University
Wenxian YU
Shanghai Jiao Tong University
Zhen TAN
Shanghai Jiao Tong University
Keyword
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Wentao LV, Junfeng WANG, Wenxian YU, Zhen TAN, "Improvement of Semi-Random Measurement Matrix for Compressed Sensing" in IEICE TRANSACTIONS on Fundamentals,
vol. E97-A, no. 6, pp. 1426-1429, June 2014, doi: 10.1587/transfun.E97.A.1426.
Abstract: In compressed sensing, the design of the measurement matrix is a key work. In order to achieve a more precise reconstruction result, the columns of the measurement matrix should have better orthogonality or linear incoherence. A random matrix, like a Gaussian random matrix (GRM), is commonly adopted as the measurement matrix currently. However, the columns of the random matrix are only statistically-orthogonal. By substituting an orthogonal basis into the random matrix to construct a semi-random measurement matrix and by optimizing the mutual coherence between dictionary columns to approach a theoretical lower bound, the linear incoherence of the measurement matrix can be greatly improved. With this optimization measurement matrix, the signal can be reconstructed from its measures more precisely.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E97.A.1426/_p
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@ARTICLE{e97-a_6_1426,
author={Wentao LV, Junfeng WANG, Wenxian YU, Zhen TAN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Improvement of Semi-Random Measurement Matrix for Compressed Sensing},
year={2014},
volume={E97-A},
number={6},
pages={1426-1429},
abstract={In compressed sensing, the design of the measurement matrix is a key work. In order to achieve a more precise reconstruction result, the columns of the measurement matrix should have better orthogonality or linear incoherence. A random matrix, like a Gaussian random matrix (GRM), is commonly adopted as the measurement matrix currently. However, the columns of the random matrix are only statistically-orthogonal. By substituting an orthogonal basis into the random matrix to construct a semi-random measurement matrix and by optimizing the mutual coherence between dictionary columns to approach a theoretical lower bound, the linear incoherence of the measurement matrix can be greatly improved. With this optimization measurement matrix, the signal can be reconstructed from its measures more precisely.},
keywords={},
doi={10.1587/transfun.E97.A.1426},
ISSN={1745-1337},
month={June},}
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TY - JOUR
TI - Improvement of Semi-Random Measurement Matrix for Compressed Sensing
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1426
EP - 1429
AU - Wentao LV
AU - Junfeng WANG
AU - Wenxian YU
AU - Zhen TAN
PY - 2014
DO - 10.1587/transfun.E97.A.1426
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E97-A
IS - 6
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - June 2014
AB - In compressed sensing, the design of the measurement matrix is a key work. In order to achieve a more precise reconstruction result, the columns of the measurement matrix should have better orthogonality or linear incoherence. A random matrix, like a Gaussian random matrix (GRM), is commonly adopted as the measurement matrix currently. However, the columns of the random matrix are only statistically-orthogonal. By substituting an orthogonal basis into the random matrix to construct a semi-random measurement matrix and by optimizing the mutual coherence between dictionary columns to approach a theoretical lower bound, the linear incoherence of the measurement matrix can be greatly improved. With this optimization measurement matrix, the signal can be reconstructed from its measures more precisely.
ER -
English
