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arXiv · 2604.16793

AstroSURE: Learning to Remove Noise from Astronomical Images Without Ground Truth Data

Omid Vaheb·Sebastien Fabbro·Stark Draper
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Abstract

In astronomical imaging, the low photon count of exposures necessitates extensive post-processing steps, including contamination removal and denoising. This paper evaluates deep-learning denoising methods that can be trained without clean ground-truth images and assesses their utility for detection11 oriented analysis of astronomical data. We adapt and compare Noise2Noise, Stein's Unbiased Risk Estimator, and blind-spot-based methods using synthetic data and real observations from the Hubble Space Telescope (HST) and the Canada-France-Hawaii Telescope (CFHT). Performance is evaluated using object-detection metrics, including correct detection rate and false alarm rate, together with image-based metrics and pixel-distribution diagnostics. The results show that these methods can improve faint-source detectability relative to the original noisy images, with encouraging gains on HST data after domain-consistent initialization, while transfer to CFHT data is more limited, highlighting the importance of instrument/domain similarity for unsupervised adaptation.

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Omid Vaheb, Sebastien Fabbro, Stark Draper. 2026-04-18. AstroSURE: Learning to Remove Noise from Astronomical Images Without Ground Truth Data. https://arxiv.org/abs/2604.16793

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