Awesome
EAVSR (CVPRW 2023)
PyTorch implementation of Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution
1. MVSR4× Datasets
<p align="center"><img src="./fig/dataset.png" width="95%"></p> <p align="center">Some samples of proposed MVSR4× dataset.</p>-
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
<p align="center"><img src="./fig/multiadastn.png" width="95%"></p> <p align="center">Overall pipeline of proposed MultiAdaSTN.</p>- 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
intrain_x2.sh
and then run: -
For MVSR4× dataset images (×4), modify
dataroot
intrain_x4.sh
and then run:
3.3 Inference
-
For RealVSR dataset images (×2), modify
dataroot
intest_x2.sh
and then run: -
For MVSR4× dataset images (×4), modify
dataroot
intest_x4.sh
and then run:
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!