Practical Compressive Sensing with Toeplitz and Circulant Matrices

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dc.contributor.authorYin, Wotao
dc.contributor.authorMorgan, Simon
dc.contributor.authorYang, Junfeng
dc.contributor.authorZhang, Yin
dc.date.accessioned2018-06-19T17:45:55Z
dc.date.available2018-06-19T17:45:55Z
dc.date.issued2010-01
dc.date.noteJanuary 2010
dc.description.abstractCompressive 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 difficult and/or costly to implement in hardware realizations. After summarizing recent results of how random Toeplitz and circulant matrices can be easily (or even naturally) realized in various applications, we introduce fast algorithms for reconstructing signals from incomplete Toeplitz and circulant measurements. We present computational results showing that Toeplitz and circulant matrices are not only as effective as random matrices for signal encoding, but also permit much faster signal decoding.
dc.format.extent10 pp
dc.identifier.citationYin, Wotao, Morgan, Simon, Yang, Junfeng, et al.. "Practical Compressive Sensing with Toeplitz and Circulant Matrices." (2010) <a href="https://hdl.handle.net/1911/102144">https://hdl.handle.net/1911/102144</a>.
dc.identifier.digitalTR10-01
dc.identifier.urihttps://hdl.handle.net/1911/102144
dc.language.isoeng
dc.titlePractical Compressive Sensing with Toeplitz and Circulant Matrices
dc.typeTechnical report
dc.type.dcmiText
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