Fast Video Super-Resolution Reconstruction Method Based on Motion Feature Fusion

Video super-resolution reconstruction methods based on deep learning are often faced with the problems of long time consumption or low accuracy. A video super-resolution reconstruction method based on deep residual network is proposed. It reconstructs videos with high accuracy quickly and meets the real-time requirements for low-resolution videos. Firstly, the adaptive key frame discrimination subnet is utilized to adaptively identify key frames from the video. Then, the reconstruction results of the key frames are obtained by the high precision reconstruction subnet. For non-key frames, the reconstruction results are directly gained based on the features obtained by fusing the features of the corresponding key frame and the motion estimation features between the non-key frame and the adjacent key frame. Experiments on open datasets show that videos are fast reconstructed by the proposed method with high accuracy and robustness.