  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}.
