无干净图像也能去噪，提升天文图像中微弱天体的探测能力
AstroSURE: Learning to Remove Noise from Astronomical Images Without Ground Truth Data
- 利用无真值数据的深度学习方法实现天文图像去噪
- 在哈勃望远镜数据上显著提升微弱天体检测率
- 适用于缺乏干净样本的天文观测场景
在天文成像中，由于曝光光子数低，需进行大量后期处理，包括污染去除和去噪。本文评估了无需干净真值图像即可训练的深度学习去噪方法，并考察其在面向目标检测的天文数据分析中的有效性。我们采用合成数据与哈勃空间望远镜（HST）和加拿大-法国-夏威夷望远镜（CFHT）的真实观测数据，对比了Noise2Noise、Stein's Unbiased Risk Estimator及基于盲区的方法。性能通过目标检测指标（如正确检测率、误报率）以及图像级指标和像素分布诊断进行评估。结果表明，这些方法可提升原始噪声图像中微弱源的可探测性，在经过领域一致初始化后，对HST数据取得令人鼓舞的增益，但向CFHT数据迁移效果有限，凸显了仪器/领域相似性在无监督适配中的重要性。
原文摘要 · Abstract (English)
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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