FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914536207941632 |
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| author | Liu, Shuhong Ge, Xining Cui, Ziteng Li, Liuzhuozheng Chang, Gengjia Liu, Jun Gu, Ziying Li, Dong Chu, Xuangeng Gu, Lin Harada, Tatsuya |
| author_facet | Liu, Shuhong Ge, Xining Cui, Ziteng Li, Liuzhuozheng Chang, Gengjia Liu, Jun Gu, Ziying Li, Dong Chu, Xuangeng Gu, Lin Harada, Tatsuya |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_03749 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution Liu, Shuhong Ge, Xining Cui, Ziteng Li, Liuzhuozheng Chang, Gengjia Liu, Jun Gu, Ziying Li, Dong Chu, Xuangeng Gu, Lin Harada, Tatsuya Computer Vision and Pattern Recognition 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. |
| title | FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.03749 |