  This paper tackles the problem of image deconvolution with joint estimation
of 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.
