  In this work we address the problem of rain streak removal with RAW images.
The general approach is firstly processing RAW data into RGB images and
removing rain streak with RGB images. Actually the original information of rain
in RAW images is affected by image signal processing (ISP) pipelines including
none-linear algorithms, unexpected noise, artifacts and so on. It gains more
benefit to directly remove rain in RAW data before being processed into RGB
format. To solve this problem, we propose a joint solution for rain removal and
RAW processing to obtain clean color images from rainy RAW image. To be
specific, we generate rainy RAW data by converting color rain streak into RAW
space and design simple but efficient RAW processing algorithms to synthesize
both rainy and clean color images. The rainy color images are used as reference
to help color corrections. Different backbones show that our method conduct a
better result compared with several other state-of-the-art deraining methods
focused on color image. In addition, the proposed network generalizes well to
other cameras beyond our selected RAW dataset. Finally, we give the result
tested on images processed by different ISP pipelines to show the
generalization performance of our model is better compared with methods on
color images.
