Low-light raw image denoising plays a crucial role in mobile photography, and learning-based methods have become the mainstream approach. Training the learning-based methods with synthetic data emerges as an efficient and practical alternative to paired real data. However, the quality of synthetic data is inherently limited by the low accuracy of the noise model, which decreases the performance of low-light raw image denoising. In this paper, we develop a novel framework for accurate noise modeling that learns a physics-guided noise neural proxy (PNNP) from dark frames. PNNP integrates three efficient techniques: physics-guided noise decoupling (PND), physics-guided proxy model (PPM), and differentiable distribution-oriented loss (DDL). The PND decouples the dark frame into different components and handles different levels of noise in a flexible manner, which reduces the complexity of the noise neural proxy. The PPM incorporates physical priors to effectively constrain the generated noise, which promotes the accuracy of the noise neural proxy. The DDL provides explicit and reliable supervision for noise modeling, which promotes the precision of the noise neural proxy. Extensive experiments on public low-light raw image denoising datasets and real low-light imaging scenarios demonstrate the superior performance of our PNNP framework.

翻译：低光照原始图像去噪在移动摄影中扮演着关键角色，基于学习的方法已成为主流技术。通过合成数据训练基于学习的模型，是配对真实数据的高效实用替代方案。然而，合成数据的质量本质上受限于噪声模型的低准确性，这降低了低光照原始图像去噪的性能。本文提出了一种新颖的精确噪声建模框架，该框架从暗帧中学习物理引导的噪声神经代理（PNNP）。PNNP集成了三种高效技术：物理引导的噪声解耦（PND）、物理引导的代理模型（PPM）以及可微分的分布导向损失（DDL）。PND将暗帧解耦为不同分量，并以灵活方式处理不同水平的噪声，从而降低了噪声神经代理的复杂度。PPM整合物理先验知识，有效约束生成的噪声，提升了噪声神经代理的准确性。DDL为噪声建模提供明确可靠的监督，促进了噪声神经代理的精确性。在公开的低光照原始图像去噪数据集和真实低光照成像场景上的大量实验表明，我们的PNNP框架具有卓越的性能。