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Improved prior for adaptive optics point spread function estimation from science images: Application for deconvolution

A. LauR. JL. F\'etickB. NeichelO. Beltramo-MartinT. Fusco
Mar 2023
Access to knowledge of the point spread function (PSF) of adaptiveoptics(AO)-assisted observations is still a major limitation when processing AOdata. This limitation is particularly important when image analysis requiresthe use of deconvolution methods. As the PSF is a complex and time-varyingfunction, reference PSFs acquired on calibration stars before or after thescientific observation can be too different from the actual PSF of theobservation to be used for deconvolution, and lead to artefacts in the finalimage. We improved the existing PSF-estimation method based on the so-calledmarginal approach by enhancing the object prior in order to make it more robustand suitable for observations of resolved extended objects. Our process isbased on a two-step blind deconvolution approach from the literature. The firststep consists of PSF estimation from the science image. For this, we made useof an analytical PSF model, whose parameters are estimated based on a marginalalgorithm. This PSF was then used for deconvolution. In this study, we firstinvestigated the requirements in terms of PSF parameter knowledge to obtain anaccurate and yet resilient deconvolution process using simulations. We showthat current marginal algorithms do not provide the required level of accuracy,especially in the presence of small objects. Therefore, we modified themarginal algorithm by providing a new model for object description, leading toan improved estimation of the required PSF parameters. Our method fulfills thedeconvolution requirement with realistic system configurations and differentclasses of Solar System objects in simulations. Finally, we validate our methodby performing blind deconvolution with SPHERE/ZIMPOL observations of theKleopatra asteroid.