title: Variational bayesian blind deconvolution using a total variation prior.
abstract: 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.
authors.affiliation: Department of Electrical Engineering and Computer Science, Northwestern University, IL 60208-3118, USA. sdb@northwestern.edu
authors.forename: S Derin
authors.initials: SD
authors.lastname: Babacan
authors.forename: Rafael
authors.initials: R
authors.lastname: Molina
authors.forename: Aggelos K
authors.initials: AK
authors.lastname: Katsaggelos
mesh_terms: D000465:Algorithms; D016477:Artifacts; 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