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• W2135859872 abstract "Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-Nyquist signal acquisition. When a statistical characterization of the signal is available, Bayesian inference can complement conventional CS methods based on linear programming or greedy algorithms. We perform asymptotically optimal Bayesian inference using belief propagation (BP) decoding, which represents the CS encoding matrix as a graphical model. Fast computation is obtained by reducing the size of the graphical model with sparse encoding matrices. To decode a length-N signal containing <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>K</i> large coefficients, our CS-BP decoding algorithm uses O(K log(N)) measurements and O(N log <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> (N)) computation. Finally, although we focus on a two-state mixture Gaussian model, CS-BP is easily adapted to other signal models." @default.
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• W2135859872 date "2010-01-01" @default.
• W2135859872 modified "2023-10-13" @default.
• W2135859872 title "Bayesian Compressive Sensing Via Belief Propagation" @default.
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• W2135859872 doi "https://doi.org/10.1109/tsp.2009.2027773" @default.
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