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CORR
2008
Springer

Bayesian Compressive Sensing via Belief Propagation

9 years 11 months ago
Bayesian Compressive Sensing via Belief Propagation
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- signal containing large coefficients, our CS-BP decoding algorithm uses ( log( )) measurements and ( log2( )) computation. Finally, although we focus on a two-state mixture Gaussian model, CS-BP is easily adapted to other signal models.
Dror Baron, Shriram Sarvotham, Richard G. Baraniuk
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2008
Where CORR
Authors Dror Baron, Shriram Sarvotham, Richard G. Baraniuk
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