title: Variational Bayesian image restoration based on a product of t-distributions image prior.
abstract: Image priors based on products have been recognized to offer many advantages because they allow simultaneous enforcement of multiple constraints. However, they are inconvenient for Bayesian inference because it is hard to find their normalization constant in closed form. In this paper, a new Bayesian algorithm is proposed for the image restoration problem that bypasses this difficulty. An image prior is defined by imposing Student-t densities on the outputs of local convolutional filters. A variational methodology, with a constrained expectation step, is used to infer the restored image. Numerical experiments are shown that compare this methodology to previous ones and demonstrate its advantages.
authors.affiliation: Department of Computer Science, University of Ioannina, Ioannina, Greece. chanjohn@cs.uoi.gr
authors.forename: Giannis
authors.initials: G
authors.lastname: Chantas
authors.forename: Nikolaos
authors.initials: N
authors.lastname: Galatsanos
authors.forename: Aristidis
authors.initials: A
authors.lastname: Likas
authors.forename: Michael
authors.initials: M
authors.lastname: Saunders
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique