@inproceedings{25ec93f4f792433f935669c4ac6b647d,
title = "Bayesian compressive sensing of wavelet coefficients using multiscale Laplacian priors",
abstract = "In this paper, we propose a novel algorithm for image reconstruction from compressive measurements of wavelet coefficients. By incorporating independent Laplace priors on separate wavelet sub-bands, the inhomogeneity of wavelet coefficient distributions and therefore the structural sparsity within images are modeled effectively. We model the problem by adopting a Bayesian formulation, and develop a fast greedy reconstruction algorithm. Experimental results demonstrate that the reconstruction performance of the proposed algorithm is competitive with state-of-the-art methods while outperforming them in terms of running times.",
keywords = "Bayesian methods, Compressive sensing, Signal reconstruction, Wavelet transforms",
author = "Esteban Vera and Luis Mancera and Babacan, {S. Derin} and Rafael Molina and Katsaggelos, {Aggelos K.}",
year = "2009",
doi = "10.1109/SSP.2009.5278598",
language = "English",
isbn = "9781424427109",
series = "IEEE Workshop on Statistical Signal Processing Proceedings",
pages = "229--232",
booktitle = "2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09",
note = "2009 IEEE/SP 15th Workshop on Statistical Signal Processing, SSP '09 ; Conference date: 31-08-2009 Through 03-09-2009",
}