Towards General and Fast Video Derain via Knowledge Distillation
Defang Cai Pan Mu Sixian Chan Zhanpeng Shao Cong Bai
As a common natural weather condition, rain can obscure video frames and thus
affect the performance of the visual system, so video derain receives a lot of
attention. In natural environments, rain has a wide variety of streak types,
which increases the difficulty of the rain removal task. In this paper, we
propose a Rain Review-based General video derain Network via knowledge
distillation (named RRGNet) that handles different rain streak types with one
pre-training weight. Specifically, we design a frame grouping-based
encoder-decoder network that makes full use of the temporal information of the
video. Further, we use the old task model to guide the current model in
learning new rain streak types while avoiding forgetting. To consolidate the
network's ability to derain, we design a rain review module to play back data
from old tasks for the current model. The experimental results show that our
developed general method achieves the best results in terms of running speed
and derain effect.