Zhenxuan Fang, Fangfang Wu, Weisheng Dong, Xin Li, Jinjian Wu and Guangming Shi

[Paper] [Website Page]

This implementation based on BasicSR and NAFNet

1. Requirements Python 3.7 and PyTorch 1.8.0.
2. Download this repository via git

git clone https://github.com/Fangzhenxuan/UFPDeblur

or download the zip file manually.

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  
    

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}
}

The codes are built on NAFNet [1]. We thank the authors for sharing their codes.

[1] Liangyu Chen, et al. "Simple Baselines for Image Restoration." In European Conference on Computer Vision 2022.