Searching Frame-Recurrent Attentive Deformable Network for Real-Time Video Deraining.

Video deraining has become an issue of great interest since rain streaks inevitably affect video quality. Most of the existing works focus on heuristically designing the network architecture to integrate available information derived from the temporal dimension. However, their inferences take a long time, so that the practicability is somewhat ignored. To solve this problem, we develop a real-time video deraining network in a frame-recurrent manner. It includes a fast attentive deformable alignment module and an automatically-discovered spatial-temporal reconstruction module. In which, the alignment is composed of a single newly-built deformable convolution under the channel attention mechanism to keep the accurate motion consistency and reduce time-consuming by a wide margin. The reconstruction part for the first time introduces the architecture search technique for video deraining to automatically discover a high-effective architecture by designing an effective and compact search space. Experimental results demonstrate remarkable superiority both in computational efficiency and actual performance compared to other state-of-the-art approaches.