Instrument parameter estimation in bayesian convex deconvolution

This paper proposes a Bayesian approach for estimation of instrument parameter in convex image deconvolution. The parameters of the instrument response (PSF) are jointly estimated with the image leading to a myopic deconvolution approach. In addition a special convex field allowing efficient hyperparameter estimation is used. The solution is based on a global a posteriori law for unknown parameters and object. The estimate is chosen in the sense of the posterior mean, numerically calculated by means of a Monte-Carlo Markov chain algorithm. The computation is efficient with a partial implementation in Fourier space. Simulation results are provided to assess the effectiveness of the proposed approach.