Dataset and Code for RealVSR

Real-world Video Super-resolution: A Benchmark Dataset and A Decomposition based Learning Scheme \ Xi Yang, Wangmeng Xiang, Hui Zeng and Lei Zhang \ International Conference on Computer Vision, 2021.

Dataset

The dataset is hosted on Google Drive and Baidu Drive (code: 43ph). Some example scenes are shown below.

The structure of the dataset is illustrated below.

Code

Dependencies

• Linux (tested on Ubuntu 18.04)
• Python 3 (tested on python 3.7)
• NVIDIA GPU + CUDA (tested on CUDA 10.2 and 11.1)

Installation

# Create a new anaconda python environment (realvsr)
conda create -n realvsr python=3.7 -y

# Activate the created environment
conda activate realvsr

# Install dependencies
pip install -r requirements.txt

# Bulid the DCN module
cd codes/models/archs/dcn
python setup.py develop

Training

Modify the configuration files accordingly in codes/options/train folder and run the following command (current we did not implement distributed training):

python train.py -opt xxxxx.yml

Testing

Test on RealVSR testing set sequences:

Modify the configuration in test_RealVSR_wi_GT.py and run the following command:

python test_RealVSR_wi_GT.py

Test on real-world captured sequences:

Modify the configuration in test_RealVSR_wo_GT.py and run the following command:

python test_RealVSR_wo_GT.py

Pre-trained Models

Some pretrained models could be found on Google Drive and Baidu Drive (code: n1n0).

License

This project is released under the Apache 2.0 license.

Citation

If you find this code useful in your research, please consider citing:

@article{yang2021real,
  title={Real-world Video Super-resolution: A Benchmark Dataset and A Decomposition based Learning Scheme},
  author={YANG, Xi and Xiang, Wangmeng and Zeng, Hui and Zhang, Lei},
  journal=ICCV,
  year={2021}
}

Acknowledgement

This implementation largely depends on EDVR. Thanks for the excellent codebase! You may also consider migrating it to BasicSR.