@inproceedings{76ddf2ede1414bae9cc9de9eb89bea38,
title = "A general sparse image prior combination in Compressed Sensing",
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.",
keywords = "Bayesian inference, Bayesian modeling, compressed sensing, image processing, millimeter wave imaging",
author = "Jorge Rubio and Miguel Vega and Rafael Molina and Katsaggelos, {Aggelos K.}",
year = "2013",
language = "English (US)",
isbn = "9780992862602",
series = "European Signal Processing Conference",
publisher = "European Signal Processing Conference, EUSIPCO",
booktitle = "2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013",
note = "2013 21st European Signal Processing Conference, EUSIPCO 2013 ; Conference date: 09-09-2013 Through 13-09-2013",
}