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.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: S. Derin Babacan, Rafael Molina, Aggelos K. Katsaggelos
• Keywords: ICASSP 2009, Signal Processing, State-of-the-art Cs Reconstruction, Synthetic 1D Signals, Unknown Signal