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ICASSP
2009
IEEE

Fast bayesian compressive sensing using Laplace priors

10 years 4 months ago
Fast bayesian compressive sensing using Laplace priors
In this paper we model the components of the compressive sensing (CS) problem using the Bayesian framework by utilizing a hierarchical form of the Laplace prior to model sparsity of the unknown signal. This signal prior includes some of the existing models as special cases and achieves a high degree of sparsity. We develop a constructive (greedy) algorithm resulting from this formulation where necessary parameters are estimated solely from the observation and therefore no user-intervention is needed. We provide experimental results with synthetic 1D signals and images, and compare with the state-of-the-art CS reconstruction algorithms demonstrating the superior performance of the proposed approach.
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag
Added 21 May 2010
Updated 21 May 2010
Type Conference
Year 2009
Where ICASSP
Authors S. Derin Babacan, Rafael Molina, Aggelos K. Katsaggelos
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