Awesome
TDAN-CVPR 2020 (Keep Update)
This is the official Pytorch implementation of TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution.
Paper | Demo Video
Usage
Main dependencies: Python 3.6 and Pytorch-0.3.1 (https://pytorch.org/get-started/previous-versions/)
$ git clone https://github.com/YapengTian/TDAN-VSR
$ compile deformable convolution functions (may be optional): bash make.sh
$ pip install -r requirements
$ python eval.py -t test_dataset_path
Citation
If you find the code helpful in your resarch or work, please cite our paper:
@article{tian2018tdan,
title={Tdan: Temporally deformable alignment network for video super-resolution},
author={Tian, Yapeng and Zhang, Yulun and Fu, Yun and Xu, Chenliang},
journal={arXiv preprint arXiv:1812.02898},
year={2018}
}
@InProceedings{tian2020tdan,
author={Tian, Yapeng and Zhang, Yulun and Fu, Yun and Xu, Chenliang},
title={TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
Resources for deformanble convolution in video restoration
TDAN present a promising framework for deformable alignment, which is shown very effective in video restoration tasks. We are super excited that our works has inspired many well-performing methods. We list a few of them for your potential reference: