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.
@article{arxiv.2605.03749,
title = {FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution},
author = {Shuhong Liu and Xining Ge and Ziteng Cui and Liuzhuozheng Li and Gengjia Chang and Jun Liu and Ziying Gu and Dong Li and Xuangeng Chu and Lin Gu and Tatsuya Harada},
journal= {arXiv preprint arXiv:2605.03749},
year = {2026}
}