Video Super-Resolution Using Non-Simultaneous Fully Recurrent Convolutional Network

Video super-resolution (SR) aims at restoring fine details and enhancing visual experience for low-resolution videos. In this paper, we propose a very deep non-simultaneous fully recurrent convolutional network for video SR. To make full use of temporal information, we employ motion compensation, very deep fully recurrent convolutional layers, and late fusion in our system. Residual connection is also employed in our recurrent structure for more accurate SR. Finally, a new model ensemble strategy is used to combine our method with a single-image SR method. Experimental results demonstrate that the proposed method is better than that of the state-of-the-art SR methods on quantitative visual quality assessment.