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This paper tackles the problem of image deconvolution with joint estimationof PSF parameters and hyperparameters. Within a Bayesian framework, thesolution is inferred via a global a posteriori law for unknown parameters andobject. The estimate is chosen as the posterior mean, numerically calculated bymeans of a Monte-Carlo Markov chain algorithm. The estimates are efficientlycomputed in the Fourier domain and the effectiveness of the method is shown onsimulated examples. Results show precise estimates for PSF parameters andhyperparameters as well as precise image estimates including restoration ofhigh-frequencies and spatial details, within a global and coherent approach.

Bayesian estimation of regularization and PSF parameters for Wiener-Hunt deconvolution