基于物理模型合成天文成像噪声，解决真实数据稀缺难题。
Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging
- 构建物理驱动的CCD噪声生成框架，涵盖光子噪声等五类噪声。
- 通过堆叠未对齐曝光生成高信噪比基底，合成大量配对数据。
- 提升图像去噪效果，适用于天体测光与科学分析任务。
天文成像在实际观测条件下仍受噪声限制。标准校准流程可消除结构化伪影，但难以处理随机噪声。尽管基于学习的去噪方法潜力巨大，但受限于稀缺的配对训练数据及科学工作流对物理可解释性的要求。本文提出一种面向望远镜CCD噪声形成的物理基础噪声合成框架，建模光子散粒噪声、响应非均匀性、暗电流噪声、读出效应以及宇宙射线和热像素引起的局部异常点。为获得低噪声输入，通过堆叠多个未对齐曝光生成高信噪比基底。利用该噪声模型从基底合成逼真的噪声图像，从而构建丰富的监督学习配对数据集。在来自两台地面望远镜的真实多波段数据集上的大量实验表明，该框架在测光精度和科学准确性方面均具显著有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。