Propagating Difference Flows for Efficient Video Super-Resolution
Ruisheng Gao (University of Science and Technology of China), Zeyu Xiao (University of Science and Technology of China), Zhiwei Xiong (University of Science and Technology of China)*
The 33rd British Machine Vision Conference
Abstract
Recent years have witnessed the advancement of video super-resolution (VSR) with elaborately-designed multi-frame alignment and space-time fusion/refinement techniques. However, both techniques require heavy computational burden and memory consumption, hindering existing VSR networks from being deployed on resource-constrained platforms (e.g., smartphones and wearable devices). In this paper, we propose an efficient and lightweight VSR network with two special designs. First, we propose a novel motion propagation scheme which propagates difference flows for efficient feature alignment. The difference flow is sparse and computational-friendly which focuses on texture details. After estimating the preliminary difference flow with an initial motion estimator, we then design an adaptive motion modification module for frame-pair wise adaptation through bidirectional propagation. Second, a dense feature distillation module is designed for further refining the aligned features efficiently. Thanks to both designs, our network achieves comparable performance with state-of-the-art VSR methods while enjoying a clear advantage in model size and computational efficiency.Video
Citation
@inproceedings{Gao_2022_BMVC,
author = {Ruisheng Gao and Zeyu Xiao and Zhiwei Xiong},
title = {Propagating Difference Flows for Efficient Video Super-Resolution},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0060.pdf}
}
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