DocumentCode :
3050109
Title :
Compressed sensing and reconstruction with bernoulli matrices
Author :
Zhang, Gesen ; Jiao, Shuhong ; Xu, Xiaoli ; Wang, Lan
Author_Institution :
Inf. & Commun. Eng. Coll., Harbin Eng. Univ., Harbin, China
fYear :
2010
fDate :
20-23 June 2010
Firstpage :
455
Lastpage :
460
Abstract :
Compressed sensing seeks to recover a sparse or compressible signal from a small number of linear and non-adaptive measurements. While most of the studies so far focus on the prominent Gaussian random measurements, we investigate the performances of matrices with Bernoulli distribution. As extensions of symmetric signs ensemble, random binary ensemble and semi-Hadamard ensemble are proposed as sensing matrices with simplex structures. Based on some results of symmetric signs ensemble and the concept of compressed sensing matrices, we obtain a theoretical result that signal compressed sensing using random binary matrices can be exactly reconstructed with high probability. In reconstruction processes, the fast and low-consumed orthogonal matching pursuit is adopted. Numerical results show that such matrices perform equally well to the Gaussian matrices.
Keywords :
iterative methods; matrix algebra; signal reconstruction; time-frequency analysis; Bernoulli distribution; Bernoulli matrices; Gaussian matrices; orthogonal matching pursuit; random binary ensemble; random binary matrices; semi-Hadamard ensemble; signal compressed sensing; symmetric signs ensemble; Automation; Compressed sensing; Image coding; Image reconstruction; Image sampling; Matching pursuit algorithms; Performance evaluation; Sparse matrices; Symmetric matrices; Vectors; Bernoulli matrices; compressed sensing; orthogonal matching pursuit; random binary ensemble; semi-Hadamard ensemble;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Automation (ICIA), 2010 IEEE International Conference on
Conference_Location :
Harbin
Print_ISBN :
978-1-4244-5701-4
Type :
conf
DOI :
10.1109/ICINFA.2010.5512379
Filename :
5512379
Link To Document :
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