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.

通过学习物理引导噪声神经代理（PNNP）从暗帧中来精确建模噪声，我们开发了一个新颖的框架，它集成了三种高效的技术：物理引导噪声解耦（PND），物理引导代理模型（PPM），可微分的分布导向损失（DDL），以提高低光原始图像降噪的性能。