E. Vera and L. Mancera and S.D. Babacan and R. Molina and A.K. Katsaggelos, “Bayesian Compressive Sensing of Wavelet coefficients using multiscale Laplacian priors”
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@INPROCEEDINGS{,
author = {E. Vera and L. Mancera and S.D. Babacan and R. Molina and A.K. Katsaggelos},
title = {Bayesian Compressive Sensing of Wavelet coefficients using multiscale Laplacian priors},
booktitle = {IEEE Workshop on Statistical Signal Processing 2009 (SSP2009)},
year = {2009},
editor = {IEEE Signal Processing Society},
volume = {},
pages = {229-232},
month = {August},
organization = {Cardiff, Wales, UK},
url = { http://decsai.ugr.es/vip/files/conferences/ssp.pdf },
annote = {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 subbands, the inhomogeneity of wavelet coefficients distributions and therefore 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.}
}