Publication Type:Conference Paper
Source:European Signal Processing Conference 2009 (EUSIPCO'09), Glasgow, Scotland (2009)
We propose a novel Bayesian formulation for the reconstruction from compressed measurements. We demonstrate that high-sparsity
enforcing priors based on l_p-norms, with 0 < p <= 1, can be used within a Bayesian framework by majorization-minimization methods. By employing a fully Bayesian analysis of the compressed sensing system and a variational Bayesian analysis for inference, the proposed framework provides model parameter estimates along with the unknown signal, as well as estimates of the unknowns, so we can calculate their uncertainties. We also show that some existing methods can be derived as special cases of the proposed compressed sensing framework. Results demonstrate the high performance of the proposed algorithm compared to commonly used methods for compressed sensing recovery.