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Context: Characterization of instrumental effects in astronomical imaging isimportant in order to extract accurate physical information from theobservations. The measured image in a real optical instrument is usuallyrepresented by the convolution of an ideal image with a Point Spread Function(PSF). Additionally, the image acquisition process is also contaminated byother sources of noise (read-out, photon-counting). The problem of estimatingboth the PSF and a denoised image is called blind deconvolution and isill-posed.  Aims: We propose a blind deconvolution scheme that relies on imageregularization. Contrarily to most methods presented in the literature, ourmethod does not assume a parametric model of the PSF and can thus be applied toany telescope.  Methods: Our scheme uses a wavelet analysis prior model on the image and weakassumptions on the PSF. We use observations from a celestial transit, where theocculting body can be assumed to be a black disk. These constraints allow us toretain meaningful solutions for the filter and the image, eliminating trivial,translated and interchanged solutions. Under an additive Gaussian noiseassumption, they also enforce noise canceling and avoid reconstructionartifacts by promoting the whiteness of the residual between the blurredobservations and the cleaned data.  Results: Our method is applied to synthetic and experimental data. The PSF isestimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and forSDO/AIA using the 2012 Venus transit. Results show that the proposednon-parametric blind deconvolution method is able to estimate the core of thePSF with a similar quality to parametric methods proposed in the literature. Wealso show that, if these parametric estimations are incorporated in theacquisition model, the resulting PSF outperforms both the parametric andnon-parametric methods.

Non-parametric PSF estimation from celestial transit solar images using blind deconvolution