Fast total variation image restoration with parameter estimation using bayesian inference
In this paper we propose two fast Total Variation (TV) based algorithms for image restoration by utilizing variational posterior distribution approximation. The unknown image and the hyperparameters for the image and observation models are formulated and estimated simultaneously within a hierachical Bayesian framework, rendering the algorithms fully-automated without any free parameters. Experimental results demonstrate that the proposed algorithms provide restoration results competitive to existing methods in terms of image quality while achieving superior computational efficiency.