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arXiv · 2504.06463

AstroClearNet: Deep image prior for multi-frame astronomical image restoration

Yashil Sukurdeep·Fausto Navarro·Tamás Budavári
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Abstract

Recovering high-fidelity images of the night sky from blurred observations is a fundamental problem in astronomy, where traditional methods typically fall short. In ground-based astronomy, combining multiple exposures to enhance signal-to-noise ratios is further complicated by variations in the point-spread function caused by atmospheric turbulence. In this work, we present a self-supervised multi-frame method, based on deep image priors, for denoising, deblurring, and coadding ground-based exposures. Central to our approach is a carefully designed convolutional neural network that integrates information across multiple observations and enforces physically motivated constraints. We demonstrate the method's potential by processing Hyper Suprime-Cam exposures, yielding promising preliminary results with sharper restored images.

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Yashil Sukurdeep, Fausto Navarro, Tamás Budavári. 2025-04-08. AstroClearNet: Deep image prior for multi-frame astronomical image restoration. https://doi.org/10.1016/j.ascom.2025.100999

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