Learning Rain Location Prior for Nighttime Deraining and Beyond.

Most deraining methods work on day scenes while leaving nighttime deraining underexplored, where darkness and non-uniform illuminations pose additional challenges. Consequently, night rain has a quite different appearance varying by location and cannot be effectively handled. To accommodate this issue, we propose a Rain Location Prior (RLP) by implicitly learning it from rainy images to reflect rain location information and boost the performance of deraining models by prior injection. Then, we introduce a Rain Prior Injection Module (RPIM) with a multi-scale scheme to modulate it by attention and emphasize the features of rain streak areas for better injection efficiency. Finally, to alleviate the data scarcity issue and facilitate the research on nighttime deraining, we propose the GTAV-NightRain dataset by considering the interaction between rain streaks and non-uniform illuminations, and provide detailed instructions on data collection pipeline which is highly replicable and flexible to integrate challenging factors of rainy night in the future. Our method outperforms state-of-the-art backbone by 1.3dB in PSNR and generalizes better on real data such as heavy rain and the presence of glow and glaring lights. Ablation studies are conducted to validate the effectiveness of each component and we visualize RLP to show good interpretability. Moreover, we apply our method to daytime deraining and desnow to show good generalizability on other location-dependent degradations. Our method is a step forward in nighttime deraining and the GTAV-NightRain dataset may become a good complement to previous datasets. Our dataset and code are publicly available at https://github.com/zkawfanx/RLP.