title: Bayesian estimation of regularization and point spread function parameters for Wiener-Hunt deconvolution.
abstract: This paper tackles the problem of image deconvolution with joint estimation of point spread function (PSF) parameters and hyperparameters. Within a Bayesian framework, the solution is inferred via a global a posteriori law for unknown parameters and object. The estimate is chosen as the posterior mean, numerically calculated by means of a Monte Carlo Markov chain algorithm. The estimates are efficiently computed in the Fourier domain, and the effectiveness of the method is shown on simulated examples. Results show precise estimates for PSF parameters and hyperparameters as well as precise image estimates including restoration of high frequencies and spatial details, within a global and coherent approach.
authors.affiliation: Laboratoire des Signaux et Systèmes (CNRS-SUPELEC-Univ. Paris-Sud 11), SUPELEC, Plateau de Moulon,3 rue Joliot-Curie, 91 192 Gif-sur-Yvette, France. orieux@lss.supelec.fr
authors.forename: François
authors.initials: F
authors.lastname: Orieux
authors.forename: Jean-François
authors.initials: JF
authors.lastname: Giovannelli
authors.forename: Thomas
authors.initials: T
authors.lastname: Rodet