用物理约束提升天文图像超分辨率，避免虚假细节。
FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution
- 基于像素空间流匹配，融合观测不确定性和源区域重要性权重。
- 在19,500对真实图像上实现更优的光度与科学准确性。
- 无需训练即可在测试时抑制幻觉，适合天文学家使用。
地基天文超分辨率需从受像素采样和大气视宁度限制的地面观测中恢复太空级图像，后者具有随机且空间变化的点扩散函数（PSF），仅靠上采样无法解决。现有方法依赖合成训练对，难以捕捉真实大气统计特性，易导致图像过度平滑或生成无物理依据的幻觉源。我们提出FluxFlow，一种保守的像素空间流匹配框架，在训练中引入观测不确定性与源区域重要性权重，并采用免训练的维纳正则化测试时修正，以抑制幻觉源同时保留细节。我们进一步构建了DESI--HST数据集，这是首个大规模真实世界基准，包含19,500对真实共注册的地基-太空图像对，涵盖真实大气PSF变化。实验表明，FluxFlow在光度与科学准确性上持续优于现有基线方法。
原文摘要 · Abstract (English)
Ground-to-space astronomical super-resolution requires recovering space-quality images from ground-based observations that are simultaneously limited by pixel sampling resolution and atmospheric seeing, which imposes a stochastic, spatially varying PSF that cannot be resolved through upsampling alone. Existing methods rely on synthetic training pairs that fail to capture real atmospheric statistics and are prone to either over-smoothed reconstructions or hallucination sources with no physical counterpart in the observed sky. We propose FluxFlow, a conservative pixel-space flow-matching framework that incorporates observation uncertainty and source-region importance weights during training, and a training-free Wiener-regularized test-time correction to suppress hallucination sources while preserving recovered detail. We further construct the DESI--HST Dataset, the large-scale real-world benchmark comprising 19,500 real co-registered ground-to-space image pairs with real atmospheric PSF variation. Experiments demonstrate that FluxFlow consistently outperforms existing baseline methods in both photometric and scientific accuracy.
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