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

• URL: http://arxiv.org/abs/2504.06463v1
• Date: Tue, 08 Apr 2025 22:07:00 GMT
• Title: AstroClearNet: Deep image prior for multi-frame astronomical image restoration
• Authors: Yashil Sukurdeep, Fausto Navarro, Tamás Budavári,
• Abstract summary: Ground-based astronomy combines multiple exposures to enhance signal-to-noise ratios.<n>We present a self-supervised multi-frame method, based on deep image priors, for denoising, deblurring, and coadding ground-based exposures.<n>We demonstrate the method's potential by processing Hyper Suprime-Cam exposures, yielding promising preliminary results with sharper restored images.
• Score: 1.2289361708127877
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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