AI RESEARCH
AstroSURE: Learning to Remove Noise from Astronomical Images Without Ground Truth Data
arXiv CS.CV
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ArXi:2604.16793v1 Announce Type: cross 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.