论文标题
视频超分辨率与暂时的集体关注
Video Super-resolution with Temporal Group Attention
论文作者
论文摘要
视频超分辨率旨在从其相应的低分辨率版本中制作出高分辨率的视频，最近引起了人们的关注。在这项工作中，我们提出了一种新颖的方法，可以以层次结构有效地合并时间信息。输入序列分为几组，每个组对应于一种帧速率。这些组提供了互补的信息，以在参考框架中恢复缺失的细节，该信息与注意模块和深层组内融合模块进一步集成在一起。此外，还提出了快速的空间对齐方式来处理大型运动的视频。广泛的结果证明了所提出的模型在处理视频各种运动中的能力。它可以在几个基准数据集上针对最新方法的最先进方法的性能。
Video super-resolution, which aims at producing a high-resolution video from its corresponding low-resolution version, has recently drawn increasing attention. In this work, we propose a novel method that can effectively incorporate temporal information in a hierarchical way. The input sequence is divided into several groups, with each one corresponding to a kind of frame rate. These groups provide complementary information to recover missing details in the reference frame, which is further integrated with an attention module and a deep intra-group fusion module. In addition, a fast spatial alignment is proposed to handle videos with large motion. Extensive results demonstrate the capability of the proposed model in handling videos with various motion. It achieves favorable performance against state-of-the-art methods on several benchmark datasets.
