[Submitted on 5 May 2026 (v1), last revised 5 Nov 2026 (this version, v3)]
Authors:Shuhong Liu, Xining Ge, Quanfeng Xu, Ziteng Cui, Liuzhuozheng Li, Gengjia Chang, Jun Liu, Ziying Gu, Dong Li, Xuangeng Chu, Lin Gu, Tatsuya Harada
Abstract: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. Project webpage: this https URL.
Submission history
From: Xining Ge [view email]
[v1] Tue, 5 May 2026 13:32:12 UTC (8,833 KB)
[v2] Wed, 6 May 2026 20:01:00 UTC (8,828 KB)
[v3] Thu, 5 Nov 2026 18:55:19 UTC (8,761 KB)
