  The lack of large-scale real raw image denoising dataset gives rise to
challenges on synthesizing realistic raw image noise for training denoising
models. However, the real raw image noise is contributed by many noise sources
and varies greatly among different sensors. Existing methods are unable to
model all noise sources accurately, and building a noise model for each sensor
is also laborious. In this paper, we introduce a new perspective to synthesize
noise by directly sampling from the sensor's real noise. It inherently
generates accurate raw image noise for different camera sensors. Two efficient
and generic techniques: pattern-aligned patch sampling and high-bit
reconstruction help accurate synthesis of spatial-correlated noise and high-bit
noise respectively. We conduct systematic experiments on SIDD and ELD datasets.
The results show that (1) our method outperforms existing methods and
demonstrates wide generalization on different sensors and lighting conditions.
(2) Recent conclusions derived from DNN-based noise modeling methods are
actually based on inaccurate noise parameters. The DNN-based methods still
cannot outperform physics-based statistical methods.
