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Babacan, S.D.; Molina, R.; Katsaggelos, A.K.
IEEE transactions on image processing, 2009-Jan., 2009, 2009-Jan, 2009-01-00, 20090101, Letnik: 18, Številka: 1Journal Article
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.
