Abstract：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.
付利华, 孙晓威, 赵宇, 李宗刚, 黄笳倞, 王路远. 基于运动特征融合的快速视频超分辨率重构方法[J]. 模式识别与人工智能, 2019, 32(11): 1022-1031.
FU Lihua, SUN Xiaowei, ZHAO Yu, LI Zonggang, HUANG Jialiang, WANG Luyuan. Fast Video Super-Resolution Reconstruction Method Based on Motion Feature Fusion. , 2019, 32(11): 1022-1031.
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2019, Vol. 32 
