Bayesian Compressive Sensing Using Laplace Priors

In this paper, we model the components of the compressive sensing (CS) problem, i.e., the signal acquisition process, the unknown signal coefficients and the model parameters for the signal and noise using the Bayesian framework. We utilize a hierarchical form of the Laplace prior to model the spars...

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Bibliographic Details
Published in:	IEEE transactions on image processing Vol. 19; no. 1; pp. 53 - 63
Main Authors:	Babacan, S.D., Molina, R., Katsaggelos, A.K.
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.01.2010 Institute of Electrical and Electronics Engineers
Subjects:	Algorithm design and analysis Applied sciences Bayesian methods compressive sensing Detection, estimation, filtering, equalization, prediction Exact sciences and technology Image coding Image processing Image reconstruction Image sampling Image sensors Information, signal and communications theory inverse problems Reconstruction algorithms relevance vector machine (RVM) Signal and communications theory Signal processing Signal sampling Signal, noise sparse Bayesian learning Telecommunications and information theory Time measurement Bayes estimation Performance evaluation State of the art Vector method Inverse problem Learning relevance vector machine (RVM) Acquisition process Relevance criterion Bayesian methods inverse problems Greedy algorithm Bayes methods Signal detection compressive sensing sparse Bayesian learning User need
ISSN:	1057-7149, 1941-0042, 1941-0042
Online Access:	Get full text
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