Video Super Resolution Using Temporal Encoding ConvLSTM and Multi-Stage Fusion

Video super resolution is a challenging task and has attracted the attention of many researchers in recent years. In this paper, we propose a multi-stage spatio-temporal feature fusion network. Different from existing methods that only aggregate features from temporal branch once at a specified stage of network, the proposed network is organized in a multi-stage manner so that the temporal correlation in features at different stages of the network can be fully exploited. Furthermore, we propose the temporal encoding convLSTM to effectively capture the temporal information at the end of each stage. Experiments on vid4 and viemo-90K demonstrate the effectiveness of the proposed method.