 |
Adaptive optics (AO) corrected image restoration is particularly difficult, as it suffers from the lack of knowledge on the point spread function (PSF) in addition to usual difficulties. An efficient approach is to marginalize the object out of the problem and to estimate the PSF and (object and noise) hyperparameters only, before deconvolving the object using these estimates. Recent works have applied this marginal semi-blind deconvolution method, based on the Maximum A Posterior (MAP) estimator, combined to a parametric model of the PSF, to a series of AO corrected astronomical and satellite images.
However, this method does not enable one to infer global uncertainties on the estimated parameters, nor to compute posterior correlations between the sought parameters.
In this communication, we propose to use a new restoration method that allows to infer such uncertainties (Yan et al., JATIS, to appear, 2023). This method consists in choosing the Minimum Mean Square Error (MMSE) estimator and computing the latter as well as the associated uncertainties thanks to a Markov chain Monte Carlo (MCMC) algorithm.
We validate our method by means of realistic simulations in the context of an astronomical observation. Finally, we present results on an experimental image of asteroid Vesta, taken on VLT/SPHERE with the Zimpol instrument.
