  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.
