Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2022-05-18 Cooperative journals: 《计算机应用研究》
Abstract： Video super-resolution (VSR) , which aims to exploit information from multiple adjacent frames to generate a high-resolution version of a reference frame. Many existing VSR works focus on how to effectively align adjacent frames to better fuse adjacent frame information, and little research has been done on the important step of adjacent frame information fusion. To solve this problem, This paper propose a video super-resolution model based on group feedback fusion mechanism (GFFMVSR) . Specifically, after adjacent frames are aligned, the aligned video sequences are fed into the first temporal attention module. Then, the sequence is divided into several groups, and each group achieves preliminary fusion through the intra-group fusion module in turn. Next, the fusion results of different groups go through a second temporal attention module. Then, each group inputs the feedback fusion module group by group, and uses the feedback mechanism to feedback and fuse the information of different groups. Finally, the fusion result output is reconstructed. It has been verified that the model has strong information fusion ability, and is superior to the existing models in both objective evaluation indicators and subjective visual effects.
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