Simultaneous Bayesian compressive sensing and blind deconvolution

Leonidas Spinoulas*, Bruno Amizic, Miguel Vega, Rafael Molina, Aggelos K Katsaggelos

*Corresponding author for this work

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

13 Scopus citations


The idea of compressive sensing in imaging refers to the reconstruction of an unknown image through a small number of incoherent measurements. Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel is unknown. In this paper, we combine these two problems trying to estimate the unknown sharp image and blur kernel solely through the compressive sensing measurements of a blurred image. We present a novel algorithm for simultaneous image reconstruction, restoration and parameter estimation. Using a hierarchical Bayesian modeling followed by an Expectation-Minimization approach we estimate the unknown image, blur and hyperparameters of the global distribution. Experimental results on simulated blurred images support the effectiveness of our method. Moreover, real passive millimeter-wave images are used for evaluating the proposed method as well as strengthening its practical aspects.

Original languageEnglish (US)
Title of host publicationProceedings of the 20th European Signal Processing Conference, EUSIPCO 2012
Number of pages5
StatePublished - Nov 27 2012
Event20th European Signal Processing Conference, EUSIPCO 2012 - Bucharest
Duration: Aug 27 2012Aug 31 2012


Other20th European Signal Processing Conference, EUSIPCO 2012


  • bayesian method
  • blind deconvolution
  • blur kernel
  • Compressive sensing
  • passive millimeter wave images

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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