Recurrent feature supplementation network for video super-resolution

Efficient aggregation of temporal information is the basis for achieving video super-resolution. Most researchers have employed alignment or propagation to exploit the temporal information of consecutive frames. However, they frequently overlook the centrality of the reference frame in the model reconstruction when using temporal features. Thus, in this paper, we design a novel recurrent feature supplementation network. We divide the temporal information into three parts: surrounding, back propagation and forward propagation, and extract and fuse them separately. A new grouping approach is proposed for extracting features from the reference frame and its surroundings. The backward temporal fusion module and the forward temporal fusion module are designed to aggregate the backward and forward temporal information at a distance. The temporal fusion module is designed to aggregate temporal information from different parts. Moreover, we propose a feature supplementation mechanism to improve the stability of the model. The feature supplement module is devised to improve the utilization of input features and the stability of the model. Experiments demonstrate that our model achieves the state-of-the-art performance.