@inproceedings{b09ff76809a94f70a775d8ee751271e9,
title = "Fast Bayesian compressive sensing using Laplace priors",
abstract = "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.",
keywords = "Bayesian methods, Compressive sensing, Inverse problems, Relevance vector machine (RVM), Sparse Bayesian learning",
author = "Babacan, {S. Derin} and Rafael Molina and Katsaggelos, {Aggelos K.}",
year = "2009",
doi = "10.1109/ICASSP.2009.4960223",
language = "English (US)",
isbn = "9781424423545",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "2873--2876",
booktitle = "2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009",
note = "2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 ; Conference date: 19-04-2009 Through 24-04-2009",
}