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SOF-VSR (Super-resolving Optical Flow for Video Super-Resolution)

Pytorch implementation of our ACCV 2018 paper "Learning for Video Super-Resolution through HR Optical Flow Estimation" and TIP 2020 paper "Deep Video Super-Resolution using HR Optical Flow Estimation".

[ACCV] [TIP]

Overview

overview

Figure 1. Overview of our SOF-VSR network.

<img width="500" src="https://github.com/LongguangWang/SOF-VSR/blob/master/Figs/temporal_profiles.png"/></div>

Figure 2. Comparison with the state-of-the-arts.

Requirements

Datasets

We collect 145 1080P video clips from the CDVL Database for training.

We use the Vid4 dataset and a subset of the DAVIS dataset (namely, DAVIS-10) for benchmark test.

Train & Test

[ACCV]

[TIP]

Results

Vid4

Figure 3. Comparative results achieved on the Vid4 dataset. Zoom-in regions from left to right: IDNnet, VSRnet, TDVSR, our SOF-VSR, DRVSR and our SOF-VSR-BD.

DAVIS-10

Figure 4. Comparative results achieved on the DAVIS-10 dataset. Zoom-in regions from left to right: IDNnet, VSRnet, our SOF-VSR, DRVSR and our SOF-VSR-BD.

temporal_profiles

Figure 5. Visual comparison of 4x SR results. From left to right: VSRnet, TDVSR, our SOF-VSR and the groundtruth.

Citation

@InProceedings{Wang2018accv,
  author    = {Longguang Wang and Yulan Guo and Zaiping Lin and Xinpu Deng and Wei An},
  title     = {Learning for Video Super-Resolution through {HR} Optical Flow Estimation},
  booktitle = {ACCV},
  year      = {2018},
}
@Article{Wang2020tip,
  author    = {Longguang Wang and Yulan Guo and Li Liu and Zaiping Lin and Xinpu Deng and Wei An},
  title     = {Deep Video Super-Resolution using {HR} Optical Flow Estimation},
  journal   = {{IEEE} Transactions on Image Processing},
  year      = {2020},
}

Contact

For questions, please send an email to wanglongguang15@nudt.edu.cn