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.

本文介绍了通过直接从传感器的真实噪声中采样来合成噪声的新视角，并提出了两种有效的技术：基于模式对齐的图像块采样和高位重建来精确合成空间相关噪声和高位噪声。对 SIDD 和 ELD 数据集进行了系统实验，结果表明，该方法表现优于现有方法，对不同传感器和光照条件具有广泛的推广性，且 DNN-based 噪声建模方法不能胜过基于物理统计方法。