Paper Info
Title
Bayesian optimal compressed sensing without priors: Parametric sure approximate message passing
Abstract
It has been shown that the Bayesian optimal approximate message passing (AMP) technique achieves the minimum mean-squared error (MMSE) optimal compressed sensing (CS) recovery. However, the prerequisite of the signal prior makes it often impractical. To address this dilemma, we propose the parametric SURE-AMP algorithm. The key feature is it uses the Stein's unbiased risk estimate (SURE) based parametric family of MMSE estimator for the CS denoising. Given that the optimization of the estimator and the calculation of its mean squared error purely depend on the noisy data, there is no need of the signal prior. The weighted sum of piecewise kernel functions is used to form the parametric estimator. Numerical experiments on both Bernoulli-Gaussian and k-dense signal justify our proposal.
Year
Venue
Keywords
2014
EUSIPCO
cs denoising,parametric sure approximate message passing,signal denoising,approximate message passing,bayesian optimal approximate message passing technique,bernoulli-gaussian signal,sure estimator,compressed sensing,parameter estimation,bayes methods,parametric estimator,estimator optimization,stein unbiased risk estimate,minimum mean-squared error optimal compressed sensing recovery,mmse estimator,signal prior,piecewise kernel functions,least mean squares methods,denoising,parametric sure-amp algorithm,bayesian optimal compressed sensing,message passing,mmse-cs,noisy data,k-dense signal
Field
DocType
ISSN
Pattern recognition,Computer science,Parametric statistics,Artificial intelligence,Prior probability,Compressed sensing,Message passing,Bayesian probability
Conference
2076-1465
Citations 
PageRank 
References 
2
0.38
0
Authors
2
Name
Order
Citations
PageRank
Chunli Guo1263.82
Mike E. Davies21664120.39