Learning Rain Location Prior for Nighttime Deraining and Beyond

Fan Zhang, Shaodi You, Yu Li, Ying Fu

Abstract: 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