  Image recognition models that work in challenging environments (e.g.,
extremely dark, blurry, or high dynamic range conditions) must be useful.
However, creating training datasets for such environments is expensive and hard
due to the difficulties of data collection and annotation. It is desirable if
we could get a robust model without the need for hard-to-obtain datasets. One
simple approach is to apply data augmentation such as color jitter and blur to
standard RGB (sRGB) images in simple scenes. Unfortunately, this approach
struggles to yield realistic images in terms of pixel intensity and noise
distribution due to not considering the non-linearity of Image Signal
Processors (ISPs) and noise characteristics of image sensors. Instead, we
propose a noise-accounted RAW image augmentation method. In essence, color
jitter and blur augmentation are applied to a RAW image before applying
non-linear ISP, resulting in realistic intensity. Furthermore, we introduce a
noise amount alignment method that calibrates the domain gap in the noise
property caused by the augmentation. We show that our proposed noise-accounted
RAW augmentation method doubles the image recognition accuracy in challenging
environments only with simple training data.
