G. Chantas and N. Galatsanos and R. Molina and A.K. Katsaggelos, “Variational Bayesian Image Restoration with a Product of Spatially Weighted Total Variation Image Priors”

In this paper a new image prior is introduced and used in image restoration. This prior is based on a generalization of the Student's-t density function and extends the Total Variation (TV) prior by making it explicitly spatially adaptive and by allowing the combination of several TV-like priors. Bayesian inference is used for image restoration with this prior via the variational approximation. The proposed algorithm is fully automatic in the sense that all necessary parameters are estimated from the data. Numerical experiments are shown which demonstrate that image restoration based on this prior compares favorably with previous state-of-the-art restoration algorithms.