Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914394748747776 |
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| author | Liu, Shuhong Ge, Xining Gu, Ziying Xu, Quanfeng Gu, Lin Cui, Ziteng Chu, Xuangeng Liu, Jun Li, Dong Harada, Tatsuya |
| author_facet | Liu, Shuhong Ge, Xining Gu, Ziying Xu, Quanfeng Gu, Lin Cui, Ziteng Chu, Xuangeng Liu, Jun Li, Dong Harada, Tatsuya |
| contents | Astronomical imaging remains noise-limited under practical observing conditions. Standard calibration pipelines remove structured artifacts but largely leave stochastic noise unresolved. Although learning-based denoising has shown strong potential, progress is constrained by scarce paired training data and the requirement for physically interpretable models in scientific workflows. We propose a physics-based noise synthesis framework tailored to CCD noise formation in the telescope. The pipeline models photon shot noise, photo-response non-uniformity, dark-current noise, readout effects, and localized outliers arising from cosmic-ray hits and hot pixels. To obtain low-noise inputs for synthesis, we stack multiple unregistered exposures to produce high-SNR bases. Realistic noisy counterparts synthesized from these bases using our noise model enable the construction of abundant paired datasets for supervised learning. Extensive experiments on our real-world multi-band dataset curated from two ground-based telescopes demonstrate the effectiveness of our framework in both photometric and scientific accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_23276 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging Liu, Shuhong Ge, Xining Gu, Ziying Xu, Quanfeng Gu, Lin Cui, Ziteng Chu, Xuangeng Liu, Jun Li, Dong Harada, Tatsuya Instrumentation and Methods for Astrophysics Computer Vision and Pattern Recognition Machine Learning Astronomical imaging remains noise-limited under practical observing conditions. Standard calibration pipelines remove structured artifacts but largely leave stochastic noise unresolved. Although learning-based denoising has shown strong potential, progress is constrained by scarce paired training data and the requirement for physically interpretable models in scientific workflows. We propose a physics-based noise synthesis framework tailored to CCD noise formation in the telescope. The pipeline models photon shot noise, photo-response non-uniformity, dark-current noise, readout effects, and localized outliers arising from cosmic-ray hits and hot pixels. To obtain low-noise inputs for synthesis, we stack multiple unregistered exposures to produce high-SNR bases. Realistic noisy counterparts synthesized from these bases using our noise model enable the construction of abundant paired datasets for supervised learning. Extensive experiments on our real-world multi-band dataset curated from two ground-based telescopes demonstrate the effectiveness of our framework in both photometric and scientific accuracy. |
| title | Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging |
| topic | Instrumentation and Methods for Astrophysics Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2601.23276 |