Enhanced Spatio-Temporal Interaction Learning for Video Deraining: A Faster and Better Framework
This work addresses video deraining to improve visibility and robustness for outdoor vision systems, representing an incremental advancement in the field.
The paper tackles the problem of video deraining by proposing ESTINet, a framework that enhances spatio-temporal interaction learning to improve deraining quality and speed, achieving faster performance than competitors while maintaining better results on three public datasets.
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.