On joint optimization of sensing matrix and sparsifying dictionary for robust compressed sensing systems

被引：14

作者：

Li, Gang ^{[1
]}

Zhu, Zhihui ^{[2
]}

Wu, Xinming ^{[3
]}

Hou, Beiping ^{[1
]}

机构：

[1] Zhejiang Univ Sci & Technol, Sch Automat & Elect Engn, Hangzhou 310023, Zhejiang, Peoples R China

[2] Colorado Sch Mines, Dept Elect Engn, 1500 Illinois St, Golden, CO 80401 USA

[3] Univ Texas Austin, Bur Econ Geol, Austin, TX 78713 USA

来源：

DIGITAL SIGNAL PROCESSING | 2018年 / 73卷

关键词：

Maximum likelihood estimation; Compressed sensing; Sparse representation error; Signal and image compression; ORTHOGONAL MATCHING PURSUIT; SPARSE REPRESENTATION; PROJECTION MATRIX; ALTERNATING OPTIMIZATION; SIGNAL RECONSTRUCTION; DESIGN; MINIMIZATION; INFORMATION; ALGORITHM; RECOVERY;

D O I：

10.1016/j.dsp.2017.10.023

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper deals with joint design of sensing matrix and sparsifying dictionary for compressed sensing (CS) systems. Based on the maximum likelihood estimation (MLE) principle, a preconditioned signal recovery (PSR) scheme and a novel measure are proposed. Such a measure allows us to optimize the sensing matrix and dictionary jointly. An alternating minimization-based iterative algorithm is derived for solving the corresponding optimal design problem. Simulation and experiments, carried with synthetic data and real image signals, show that the PSR scheme and the CS system, obtained using the proposed approaches, outperform the prevailing ones in terms of reducing the effect of sparse representation errors. (C) 2017 Elsevier Inc. All rights reserved.

引用

页码：62 / 71

页数：10

共 39 条

[1] A gradient-based alternating minimization approach for optimization of the measurement matrix in compressive sensing [J].

Abolghasemi, Vahid ;

Ferdowsi, Saideh ;

Sanei, Saeid .

SIGNAL PROCESSING, 2012, 92 (04) :999-1009

[2] K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation [J].

Aharon, Michal ;

Elad, Michael ;

Bruckstein, Alfred .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2006, 54 (11) :4311-4322

[3]

Bai H., 2013, P 10 INT S WIR COMM

[4] Alternating Optimization of Sensing Matrix and Sparsifying Dictionary for Compressed Sensing [J].

Bai, Huang ;

Li, Gang ;

Li, Sheng ;

Li, Qiuwei ;

Jiang, Qianru ;

Chang, Liping .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2015, 63 (06) :1581-1594

[5] Robust uncertainty principles:: Exact signal reconstruction from highly incomplete frequency information [J].

Candès, EJ ;

Romberg, J ;

Tao, T .

IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (02) :489-509

[6]

Candès EJ, 2008, IEEE SIGNAL PROC MAG, V25, P21, DOI 10.1109/MSP.2007.914731

[7] Dictionary Learning With Optimized Projection Design for Compressive Sensing Applications [J].

Chen, Wei ;

Rodrigues, Miguel R. D. .

IEEE SIGNAL PROCESSING LETTERS, 2013, 20 (10) :992-995

[8] Projection Design for Statistical Compressive Sensing: A Tight Frame Based Approach [J].

Chen, Wei ;

Rodrigues, Miguel R. D. ;

Wassell, Ian J. .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2013, 61 (08) :2016-2029

[9] Optimized projections for compressed sensing via rank-constrained nearest correlation matrix [J].

Cleju, Nicolae .

APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, 2014, 36 (03) :495-507

[10] Compressed sensing [J].

Donoho, DL .

IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (04) :1289-1306

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