Papers › Unsupervised Video Deraining with An Event Camera
Unsupervised Video Deraining with An Event Camera
Jin Wang, Wenming Weng, Yueyi Zhang, Zhiwei Xiong
Current unsupervised video deraining methods are inefficient in modeling the intricate spatio-temporal properties of rain, which leads to unsatisfactory results. In this paper, we propose a novel approach by integrating a bio-inspired event camera into the unsupervised video deraining pipeline, which enables us to capture high temporal resolution information and model complex rain characteristics. Specifically, we first design an end-to-end learning-based network consisting of two modules, the asymmetric separation module and the cross-modal fusion module. The two modules are responsible for segregating the features of the rain-background layer, and for positive enhancement and negative suppression from a cross-modal perspective, respectively. Second, to regularize the network training, we elaborately design a cross-modal contrastive learning method that leverages the complementary information from event cameras, exploring the mutual exclusion and similarity of rain-background layers in different domains. This encourages the deraining network to focus on the distinctive characteristics of each layer and learn a more discriminative representation. Moreover, we construct the first real-world dataset comprising rainy videos and events using a hybrid imaging system. Extensive experiments demonstrate the superior performance of our method on both synthetic and real-world datasets.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections