EAVSR (CVPRW 2023)

PyTorch implementation of Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution

1. MVSR4× Datasets

Some samples of proposed MVSR4× dataset.

Dataset
- we use an ultra-wide camera with a 13mm-equivalent lens and a telephoto camera with a 90mm-equivalent lens to capture LR and HR sequences, respectively.
Download
- Our MVSR4× dataset can be downloaded from this link with password 'uygh'.

2. Framework

Overall pipeline of proposed MultiAdaSTN.

Prerequisites
- Python $\geqslant$ 3.7, PyTorch $\geqslant$ 1.8 and CUDA $\geqslant$ 11.0.
- OpenCV, NumPy, tqdm, lpips, scikit-image, mmcv and tensorboardX.

3. Quick Start

3.1 Training

For RealVSR dataset images (×2), modify dataroot in train_x2.sh and then run:

sh train_x2.sh
For MVSR4× dataset images (×4), modify dataroot in train_x4.sh and then run:

sh train_x4.sh

3.3 Inference

For RealVSR dataset images (×2), modify dataroot in test_x2.sh and then run:

sh test_x2.sh
For MVSR4× dataset images (×4), modify dataroot in test_x4.sh and then run:

sh test_x4.sh

3.4 Note

You can specify which GPU to use by --gpu_ids, e.g., --gpu_ids 0,1, --gpu_ids 3, --gpu_ids -1 (for CPU mode). In the default setting, No.0 GPU is used.
You can refer to options for more arguments.

4. Citation

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

@InProceedings{Wang_2023_CVPR,
    author    = {Wang, Ruohao and Liu, Xiaohui and Zhang, Zhilu and Wu, Xiaohe and Feng, Chun-Mei and Zhang, Lei and Zuo, Wangmeng},
    title     = {Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month     = {June},
    year      = {2023},
    pages     = {1168-1177}
}

5. Acknowledgement

This repo is built upon the framework of CycleGAN, and we borrow some code from RealVSR and BasicVSR++, thanks for their excellent work!

1180300419 / EAVSR