Video deraining is an important task in computer vision as the unwanted rain hampers the visibility of videos and deteriorates the robustness of most outdoor vision systems. Despite the significant success which has been achieved for video deraining recently, two major challenges remain: 1) how to exploit the vast information among continuous frames to extract powerful spatio-temporal features across both the spatial and temporal domains, and 2) how to restore high-quality derained videos with a high-speed approach. In this paper, we present a new end-to-end video deraining framework, named Enhanced Spatio-Temporal Interaction Network (ESTINet), which considerably boosts current state-of-the-art video deraining quality and speed. The ESTINet takes the advantage of deep residual networks and convolutional long short-term memory, which can capture the spatial features and temporal correlations among continuing frames at the cost of very little computational source. Extensive experiments on three public datasets show that the proposed ESTINet can achieve faster speed than the competitors, while maintaining better performance than the state-of-the-art methods.

翻译：视频脱线是计算机愿景中的一项重要任务,因为不想要的雨阻碍视频的可见度,并使大多数室外视觉系统的稳健性恶化。尽管最近视频脱线工作取得了显著成功,但仍存在两大挑战:(1) 如何利用连续框架之间的大量信息,以在空间和时空领域提取强大的时空特征;(2) 如何以高速方法恢复高质量的脱线视频。 在本文中,我们介绍了一个新的终端到终端视频脱线框架,名为“强化空间-时空互动网络 ” ( ESTINet ), 大大提升了目前最新的视频脱线质量和速度。 ERT网络利用了深层残余网络和革命性长短期记忆的优势,这些网络可以以极小的计算来源的成本捕捉到连续框架之间的空间特征和时间相关性。 对三个公共数据集的广泛实验显示,拟议的ESTINet可以比竞争者更快的速度,同时保持比最新方法更好的性能。