Extending AMIRAL's blind deconvolution of Adaptive Optics corrected images with Markov chain Monte Carlo methods
Abstract
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 blind deconvolution method, combined to a parametric model of the PSF, to a series of AO corrected astronomical and satellite images. In this communication, we propose a new restoration method, which consists in choosing the Minimum Mean Square Error (MMSE) estimator and computing the latter thanks to a Markov chain Monte Carlo (MCMC) algorithm. We validate our method by means of realistic simulations, in two very different contexts: an astronomical observation on VLT/SPHERE and a ground-based LEO satellite observation on a 1.52m telescope. Finally, we present results on experimental images for both applications.
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BibTeX
@misc{yan2026extending,
title = {{Extending AMIRAL's blind deconvolution of Adaptive Optics corrected images with Markov chain Monte Carlo methods}},
author = {Alix Yan and Laurent Mugnier and Jean-François Giovannelli and Romain Fétick and Cyril Petit},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.29492},
eprint = {2610.29492},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.29492},
}
RIS
TY - UNPB
TI - Extending AMIRAL's blind deconvolution of Adaptive Optics corrected images with Markov chain Monte Carlo methods
AU - Alix Yan
AU - Laurent Mugnier
AU - Jean-François Giovannelli
AU - Romain Fétick
AU - Cyril Petit
PY - 2026
DA - 2026/10/21
PB - arXiv
DO - 10.48550/arxiv.2610.29492
UR - https://arxiv.org/abs/2610.29492
AB - 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 blind deconvolution method, combined to a parametric model of the PSF, to a series of AO corrected astronomical and satellite images. In this communication, we propose a new restoration method, which consists in choosing the Minimum Mean Square Error (MMSE) estimator and computing the latter thanks to a Markov chain Monte Carlo (MCMC) algorithm. We validate our method by means of realistic simulations, in two very different contexts: an astronomical observation on VLT/SPHERE and a ground-based LEO satellite observation on a 1.52m telescope. Finally, we present results on experimental images for both applications.
ER -