Video Super-resolution with Temporal Group Attention

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Authors Qi Tian, Shengjin Wang, Chunjing Xu, Ya-Li Li, Xu Jia, Takashi Isobe, Songjiang Li, Shanxin Yuan, Gregory Slabaugh
Journal/Conference Name CVPR 2020 6
Paper Category
Paper Abstract 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.
Date of publication 2020
Code Programming Language Unspecified
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