Video super-resolution using motion compensation and residual bidirectional recurrent convolutional network
Dingyi Li, Yü Liu, Zengfu Wang · 2017
Video super-resolution (SR) aims at restoring finer details and enhancing visual experience. In this paper, we propose a novel method named residual recurrent convolutional network (RRCN) for video SR. In our method, motion compensation and bidirectional residual convolutional network are combined to model the spatial and temporal non-linear mappings. To leverage sufficient amount of temporal information, we employ motion compensation, bidirectional recurrent convolutional layers and late fusion in of our network. We also apply residual connections in our recurrent structure for more accurate SR. Experimental results demonstrate the superiority of the proposed method over state-of-the-art single-image and multi-frame based SR approaches in terms of both quantitative assessment and visual quality.