Paper Info
Title
Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning
Abstract
Spatial-Temporal Video Super-Resolution (ST-VSR) aims to generate super-resolved videos with higher resolution (HR) and higher frame rate (HFR). Quite intuitively, pioneering two-stage based methods complete ST-VSR by directly combining two sub-tasks: Spatial Video Super-Resolution (S-VSR) and Temporal Video Super-Resolution (T-VSR) but ignore the reciprocal relations among them. Specifically, 1) T-VSR to S-VSR: temporal correlations help accurate spatial detail representation with more clues; 2) S-VSR to T-VSR: abundant spatial information contributes to the refinement of temporal prediction. To this end, we propose a one-stage based Cycle-projected Mutual learning network (CycMu-Net) for ST-VSR, which makes full use of spatial-temporal correlations via the mutual learning between S-VSR and T-VSR. Specifically, we propose to exploit the mutual information among them via iterative up-and-down projections, where the spatial and temporal features are fully fused and distilled, helping the high-quality video reconstruction. Besides extensive experiments on benchmark datasets, we also compare our proposed CycMu-Net with S-VSR and T-VSR tasks, demonstrating that our method significantly outperforms state-of-the-art methods. Codes are publicly available at: https://github.com/hhhhhumengshun/CycMuNet.
Year
DOI
Venue
2022
10.1109/CVPR52688.2022.00356
IEEE Conference on Computer Vision and Pattern Recognition
Keywords
DocType
Volume
Image and video synthesis and generation
Conference
2022
Issue
ISSN
Citations 
1
2022 CVPR
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Mengshun Hu100.34
Kui Jiang29417.91
Liang Liao301.69
Jing Xiao431227.78
Junjun Jiang5113874.49
Zheng Wang635236.33