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Bayesian Blind Deconvolution With General Sparse Image Priors


S.D. Babacan, R. Molina, M.N. Do, and A.K. Katsaggelos, “Bayesian Blind deconvolution with general sparse image priors” in European Conference on Computer Vision (ECCV), 341-355, Florence (Italy), September 2012. [BibTeX entry][ (1499 KB.)]


We present a general method for blind image deconvolution using Bayesian inference with super-Gaussian sparse image priors. We consider a large family of priors suitable for modeling natural images, and develop the general procedure for estimating the unknown image and the blur. Our formulation includes a number of existing modeling and inference methods as special cases while providing additional flexibility in image modeling and algorithm design. We also present an analysis of the proposed inference compared to other methods and discuss its advantages. Theoretical and experimental results demonstrate that the proposed formulation is very effective, efficient, and flexible.


Matlab source code.


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Visual Image Processing
University of Granada