Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging

• URL: http://arxiv.org/abs/2601.23276v1
• Date: Fri, 30 Jan 2026 18:47:54 GMT
• Title: Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging
• Authors: Shuhong Liu, Xining Ge, Ziying Gu, Lin Gu, Ziteng Cui, Xuangeng Chu, Jun Liu, Dong Li, Tatsuya Harada,
• Abstract summary: Learning-based denoising is promising, yet progress is hindered by scarce paired training data.<n>We propose a physics-based noise synthesis framework tailored to CCD noise formation.
• Score: 47.83642412662346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Astronomical imaging remains noise-limited under practical observing constraints, while standard calibration pipelines mainly remove structured artifacts and leave stochastic noise largely unresolved. Learning-based denoising is promising, yet progress is hindered by scarce paired training data and the need for physically interpretable and reproducible models in scientific workflows. We propose a physics-based noise synthesis framework tailored to CCD noise formation. 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 average 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. We further introduce a real-world dataset across multi-bands acquired with two twin ground-based telescopes, providing paired raw frames and instrument-pipeline calibrated frames, together with calibration data and stacked high-SNR bases for real-world evaluation.

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