  One of the major limitations of adaptive optics (AO) corrected image
post-processing is the lack of knowledge on the system point spread function
(PSF). The PSF is not always available as a direct imaging on isolated point
like objects such as stars. Its prediction using AO telemetry also suffers from
serious limitations and requires complex and yet not fully operational
algorithms. A very attractive solution consists in a direct PSF estimation
using the scientific images themselves thanks to blind or myopic
post-processing approaches. We demonstrate that such approaches suffer from
severe limitations when a joint restitution of object and PSF parameters is
performed. As an alternative we propose here a marginalized PSF identification
that overcomes this limitation. Then the PSF is used for image post-processing.
Here we focus on deconvolution, a post-processing technique to restore the
object, given the image and the PSF. We show that the PSF estimated by
marginalisation provides good quality deconvolution. The full process of
marginalized PSF estimation and deconvolution constitutes a successful blind
deconvolution technique. It is tested on simulated data to measure its
performance. It is also tested on experimental adaptive optics images of the
asteroid 4-Vesta by VLT/SPHERE/Zimpol to demonstrate application to on-sky
data.
