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Variational Bayesian Blind Deconvolution Using a Total Variation Prior

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3 Author(s)
Babacan, S.D. ; Dept. of Electr. Eng. & Comput. Sci., Northwestern Univ., Evanston, IL ; Molina, R. ; Katsaggelos, A.K.

In this paper, we present novel algorithms for total variation (TV) based blind deconvolution and parameter estimation utilizing a variational framework. Using a hierarchical Bayesian model, the unknown image, blur, and hyperparameters for the image, blur, and noise priors are estimated simultaneously. A variational inference approach is utilized so that approximations of the posterior distributions of the unknowns are obtained, thus providing a measure of the uncertainty of the estimates. Experimental results demonstrate that the proposed approaches provide higher restoration performance than non-TV-based methods without any assumptions about the unknown hyperparameters.

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Image Processing, IEEE Transactions on  (Volume:18 ,  Issue: 1 )