@inproceedings{Jorge2013,
abstract = {In this paper a general combination of sparse image priors is applied to Bayesian Compressed Sensing (CS) reconstruction of digital images. A simultaneous deblurring and CS reconstruction variational algorithm is derived. The application of the new algorithm, to both blurred and non-blurred images at different compression ratios, is studied. The new method is applied to Passive Millimeter-Wave Imaging (PMWI) CS. and its performance compared to state of the art CS reconstruction methods. {\textcopyright} 2013 EURASIP.},
author = {Rubio, Jorge and Vega, Miguel and Molina, Rafael and Katsaggelos, Aggelos K.},
booktitle = {European Signal Processing Conference},
isbn = {9780992862602},
issn = {22195491},
keywords = {Bayesian inference,Bayesian modeling,compressed sensing,image processing,millimeter wave imaging},
pages = {1--5},
title = {{A general sparse image prior combination in Compressed Sensing}},
year = {2013}
}

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