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

TL;DR

This paper presents a Bayesian method for joint estimation of PSF parameters and hyperparameters in image deconvolution, using Monte Carlo techniques to achieve precise restoration of high-frequency details.

Contribution

It introduces a Bayesian framework with a Monte Carlo Markov Chain algorithm for simultaneous estimation of PSF and hyperparameters in deconvolution, improving accuracy and efficiency.

Findings

Accurate estimation of PSF parameters and hyperparameters.

High-quality image restoration including high-frequency details.

Efficient computation in the Fourier domain.

Abstract

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.