Progressive Subtractive Recurrent Lightweight Network for Video Deraining

Abstract

Presence of rainy artifacts severely degrade the overall visual quality of a video and tend to overlap with the useful information present in the video frames. This degraded video affects the effectiveness of many automated applications like traffic monitoring, surveillance, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc.</i> As video deraining is a pre-processing step for automated applications, it is highly demanded to have a lightweight deraining module. Therefore, in this paper, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“Progressive Subtractive Recurrent Lightweight Network”</i> is proposed for video deraining. Initially, the Multi-Kernel feature Sharing Residual Block (MKSRB) is designed to learn different sizes of rain streaks which facilitates the complete removal of rain streaks through progressive subtractions. These MKSRB features are merged with previous frame output recurrently to maintain the temporal consistency. Further, multi-receptive feature subtraction is performed through Multi-scale Multi-Receptive Difference Block (MMRDB) to avoid loss of details and extract high-frequency information. Finally, progressively learned features through MKSRB and recurrent feature merging are aggregated with fused MMRDB features which outputs the rain-free frame. Substantial experiments on prevailing synthetic datasets and real-world videos verify the superior performance of the proposed method over the existing state-of-the-art methods for video deraining.