Bayesian Compressive Sensing

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Bayesian Compressive Sensing
The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M N of basis-function coefficients associated with B. Compressive sensing is a framework whereby one does not measure one of the aforementioned N-dimensional signals directly, but rather a set of related measurements, with the new measurements a linear combination of the original underlying N-dimensional signal. The number of required compressive-sensing measurements is typically much smaller than N, offering the potential to simplify the sensing system. Let f denote the unknown underlying N-dimensional signal, and g a vector of compressive-sensing measurements, then one may approximate f accurately by utilizing knowledge of the (under-determined) linear relationship between f and g, in addition to knowledge of the fact that f is compressible in B. In this paper we...
Shihao Ji, Ya Xue, Lawrence Carin
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2008
Where TSP
Authors Shihao Ji, Ya Xue, Lawrence Carin
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