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.

翻译：在实际观测条件下，天文成像仍受限于噪声，而标准校准流程主要去除结构化伪影，对随机噪声的处理效果有限。基于学习的去噪方法前景广阔，但其发展受限于配对训练数据的稀缺，以及科学工作流程中对物理可解释且可复现模型的需求。本文提出一种基于物理的噪声合成框架，专门针对CCD噪声形成机制设计。该流程建模了光子散粒噪声、光响应非均匀性、暗电流噪声、读出效应，以及宇宙射线撞击和热像素引起的局部异常值。为获得合成所需的低噪声输入，我们对多幅未配准的曝光图像进行平均处理，生成高信噪比基准图像。利用我们的噪声模型从这些基准图像合成的真实噪声对应图像，能够构建丰富的配对数据集用于监督学习。我们进一步引入一个通过两台同型地基望远镜获取的多波段真实数据集，提供配对的原始帧与仪器流水线校准帧，同时包含校准数据及叠加生成的高信噪比基准图像，用于真实场景评估。