A general sparse image prior combination in Compressed Sensing

Jorge Rubio, Miguel Vega, Rafael Molina, Aggelos K Katsaggelos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.

Original languageEnglish (US)
Title of host publication2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013
PublisherEuropean Signal Processing Conference, EUSIPCO
ISBN (Print)9780992862602
StatePublished - Jan 1 2013
Event2013 21st European Signal Processing Conference, EUSIPCO 2013 - Marrakech, Morocco
Duration: Sep 9 2013Sep 13 2013

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Other

Other2013 21st European Signal Processing Conference, EUSIPCO 2013
CountryMorocco
CityMarrakech
Period9/9/139/13/13

Keywords

  • Bayesian inference
  • Bayesian modeling
  • compressed sensing
  • image processing
  • millimeter wave imaging

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Rubio, J., Vega, M., Molina, R., & Katsaggelos, A. K. (2013). A general sparse image prior combination in Compressed Sensing. In 2013 Proceedings of the 21st European Signal Processing Conference, EUSIPCO 2013 [6811486] (European Signal Processing Conference). European Signal Processing Conference, EUSIPCO.