Awesome
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.
| File | Description |
|---|---|
| GT.zip | All ground truth sequences in RGB format |
| LQ.zip | All low quality sequences in RGB format |
| GT_YCbCr.zip | All ground truth sequences in YCbCr format |
| LQ_YCbCr.zip | All low quality sequences in YCbCr format |
| GT_test.zip | Ground truth test sequences in RGB format |
| LQ_test.zip | Low Quality test sequences in RGB format |
| GT_YCbCr_test.zip | Ground truth test sequences in YCbCr format |
| LQ_YCbCr_test.zip | Low Quality test sequences in YCbCr format |
| videos.zip | Original videos (> 500 LR-HR pairs here) |
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.