Awesome
Self-supervised Non-uniform Kernel Estimation with Flow-based Motion Prior for Blind Image Deblurring (CVPR 2023)
Zhenxuan Fang, Fangfang Wu, Weisheng Dong, Xin Li, Jinjian Wu and Guangming Shi
Architecture
<p align="center"> <img src="/illustrations/network.png" width="1200"> </p> Fig. 1 The overall framework of the proposed UFPNet for blind SR.Usage
This implementation based on BasicSR and NAFNet
Download the repository
- Requirements
Python 3.7 and PyTorch 1.8.0.
- Download this repository via git
git clone https://github.com/Fangzhenxuan/UFPDeblur
or download the zip file manually.
Quick Start
Download the pretrained checkpoints (Google Drive), the directory structure will be arranged as:
experiments
|- pretrained_models
|- train_on_GoPro
|- train_on_RealBlurJ
|- train_on_RealBlurR
Put the test datasets in dir ./datasets/
datasets
|- GoPro
|- test
|- target
|- input
|- ...
- Test on GoPro testset, run
python ./basicsr/test.py -opt options/test/GoPro/UFPNet-GoPro.yml
- Test on RealBlur-J testset
- To use the model trained on GoPro, run
python ./basicsr/test.py -opt options/test/RealBlur-J/UFPNet-RealBlurJ-Train-on-GoPro.yml
- To use the model trained on RealBlur-J, run
python ./basicsr/test.py -opt options/test/RealBlur-J/UFPNet-RealBlurJ.yml
- To use the model trained on GoPro, run
Citations
If UFPNet helps your research or work, please consider citing UFPNet.
@inproceedings{fang2023self,
title={Self-supervised Non-uniform Kernel Estimation with Flow-based Motion Prior for Blind Image Deblurring},
author={Fang, Zhenxuan and Wu, Fangfang and Dong, Weisheng and Li, Xin and Wu, Jinjian and Shi, Guangming},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={18105--18114},
year={2023}
}
Acknowledgements
The codes are built on NAFNet [1]. We thank the authors for sharing their codes.
References
[1] Liangyu Chen, et al. "Simple Baselines for Image Restoration." In European Conference on Computer Vision 2022.