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Proceedings Paper

Practical compressive sensing with Toeplitz and circulant matrices
Author(s): Wotao Yin; Simon Morgan; Junfeng Yang; Yin Zhang
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Paper Abstract

Compressive sensing encodes a signal into a relatively small number of incoherent linear measurements. In theory, the optimal incoherence is achieved by completely random measurement matrices. However, such matrices are often difficult and costly to implement in hardware realizations. Random Toeplitz and circulant matrices can be easily (or even naturally) realized in various applications. This paper introduces fast algorithms for reconstructing signals from incomplete Toeplitz and circulant measurements. Computational results are presented to show that Toeplitz and circulant matrices are not only as effective as random matrices for signal encoding, but also permit much faster decoding.

Paper Details

Date Published: 14 July 2010
PDF: 10 pages
Proc. SPIE 7744, Visual Communications and Image Processing 2010, 77440K (14 July 2010); doi: 10.1117/12.863527
Show Author Affiliations
Wotao Yin, Rice Univ. (United States)
Simon Morgan, New Mexico Consortium (United States)
Junfeng Yang, Nanjing Univ. (China)
Yin Zhang, Rice Univ. (United States)

Published in SPIE Proceedings Vol. 7744:
Visual Communications and Image Processing 2010
Pascal Frossard; Houqiang Li; Feng Wu; Bernd Girod; Shipeng Li; Guo Wei, Editor(s)

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