DocumentCode
2054711
Title
A general sparse image prior combination in Compressed Sensing
Author
Rubio, Jesus ; Vega, M. ; Molina, Rafael ; Katsaggelos, Aggelos K.
Author_Institution
Univ. de Granada, Granada, Spain
fYear
2013
fDate
9-13 Sept. 2013
Firstpage
1
Lastpage
5
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
belief networks; compressed sensing; data compression; image restoration; millimetre wave imaging; variational techniques; Bayesian CS reconstruction variational algorithm; PMWI CS; compressed sensing; deblurring algorithm; digital image processing; general sparse image prior; nonblurred image; passive millimeter wave imaging; Abstracts; Gaussian noise; Imaging; Bayesian inference; Bayesian modeling; compressed sensing; image processing; millimeter wave imaging;
fLanguage
English
Publisher
ieee
Conference_Titel
Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European
Conference_Location
Marrakech
Type
conf
Filename
6811486
Link To Document