Recently, the mainstream practice for training low-light raw image denoising methods has shifted towards employing synthetic data. Noise modeling, which focuses on characterizing the noise distribution of real-world sensors, profoundly influences the effectiveness and practicality of synthetic data. Currently, physics-based noise modeling struggles to characterize the entire real noise distribution, while learning-based noise modeling impractically depends on paired real data. In this paper, we propose a novel strategy: learning the noise model from dark frames instead of paired real data, to break down the data dependency. Based on this strategy, we introduce an efficient physics-guided noise neural proxy (PNNP) to approximate the real-world sensor noise model. Specifically, we integrate physical priors into neural proxies and introduce three efficient techniques: physics-guided noise decoupling (PND), physics-guided proxy model (PPM), and differentiable distribution loss (DDL). PND decouples the dark frame into different components and handles different levels of noise flexibly, which reduces the complexity of noise modeling. PPM incorporates physical priors to constrain the generated noise, which promotes the accuracy of noise modeling. DDL provides explicit and reliable supervision for noise distribution, which promotes the precision of noise modeling. PNNP exhibits powerful potential in characterizing the real noise distribution. Extensive experiments on public datasets demonstrate superior performance in practical low-light raw image denoising. The code will be available at \url{https://github.com/fenghansen/PNNP}.

翻译：近期，训练低光照原始图像去噪方法的主流实践已转向使用合成数据。噪声建模专注于描述真实传感器的噪声分布，深刻影响着合成数据的有效性和实用性。当前，基于物理的噪声建模难以刻画完整的真实噪声分布，而基于学习的噪声建模则不切实际地依赖配对真实数据。本文提出一种新策略：从暗帧而非配对真实数据中学习噪声模型，以打破数据依赖性。基于该策略，我们引入一种高效的物理引导噪声神经代理（PNNP）来近似真实传感器噪声模型。具体而言，我们将物理先验融入神经代理，并引入三种高效技术：物理引导噪声解耦（PND）、物理引导代理模型（PPM）和可微分布损失（DDL）。PND将暗帧解耦为不同成分，灵活处理不同噪声水平，降低了噪声建模的复杂度。PPM融入物理先验以约束生成噪声，提升了噪声建模的准确性。DDL为噪声分布提供显式且可靠的监督，促进了噪声建模的精确性。PNNP在刻画真实噪声分布方面展现出强大潜力。在公开数据集上的大量实验证明了其在实用低光照原始图像去噪中的优越性能。代码将发布于\url{https://github.com/fenghansen/PNNP}。