Sparse Bayesian image restoration

S. Derin Babacan, Rafael Molina, Aggelos K. Katsaggelos

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

12 Scopus citations


In this paper we propose a novel Bayesian algorithm for image restoration and parameter estimation. We utilize an image prior where Gaussian distributions are placed per pixel in the high-pass filter outputs of the image. By following the hierarchical Bayesian framework, we simultaneously estimate the unknown image and hyperparameters for both the image prior and the image degradation noise. We show that the proposed formulation is a special case of the popular lp-norm based formulations with p = 0, and therefore enforces sparsity to an high extent in the filtered image coefficients. Moreover, the proposed formulation results in a convex optimization problem, and therefore does not suffer from the robustness issues common with non-convex image priors. Experimental results demonstrate that the proposed algorithm provides superior performance compared to state-of-the-art restoration algorithms although no user-supervision is required.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Number of pages4
StatePublished - 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: Sep 26 2010Sep 29 2010

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880


Other2010 17th IEEE International Conference on Image Processing, ICIP 2010
Country/TerritoryHong Kong
CityHong Kong


  • Bayesian methods
  • Image restoration
  • Parameter estimation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing


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