@inproceedings{Esteban2009,
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. {\textcopyright} 2009 IEEE.},
author = {Vera, Esteban and Mancera, Luis and Babacan, S. Derin and Molina, Rafael and Katsaggelos, Aggelos K.},
booktitle = {2009 IEEE/SP 15th Workshop on Statistical Signal Processing},
doi = {10.1109/SSP.2009.5278598},
isbn = {978-1-4244-2709-3},
keywords = {Bayesian methods,Compressive sensing,Signal reconstruction,Wavelet transforms},
month = {aug},
pages = {229--232},
publisher = {IEEE},
title = {{Bayesian compressive sensing of wavelet coefficients using multiscale Laplacian priors}},
url = {http://ieeexplore.ieee.org/document/5278598/},
year = {2009}
}

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