Deep Residual Fourier Transformation for Single Image Deblurring

Xintian Mao, Yiming Liu, Wei Shen, Qingli Li and Yan Wang

Paper: https://arxiv.org/abs/2111.11745

Network Architecture

Overall Framework of DeepRFT

Installation

The model is built in PyTorch 1.8.0 and tested on Ubuntu 18.04 environment (Python3.8, CUDA11.1).

For installing, follow these intructions

conda create -n pytorch python=3.8
conda activate pytorch
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge
pip install matplotlib scikit-image opencv-python yacs joblib natsort h5py tqdm kornia tensorboard ptflops

Install warmup scheduler

cd pytorch-gradual-warmup-lr; python setup.py install; cd ..

Quick Run

To test the pre-trained models of Deblur and Defocus Google Drive or 百度网盘(提取码:phws) on your own images, run

python test.py --weights ckpt_path_here --input_dir path_to_images --result_dir save_images_here --win_size 256 # deblur
python test.py --weights ckpt_path_here --input_dir path_to_images --result_dir save_images_here --win_size 512 # defocus

Here is an example to train:

python train.py

Results

Experiment for image deblurring.

Deblurring on GoPro Datasets.

Reference Code:

Citation

If you use DeepRFT, please consider citing:

@inproceedings{,
    title={Deep Residual Fourier Transformation for Single Image Deblurring},
    author={Xintian Mao, Yiming Liu, Wei Shen, Qingli Li, Yan Wang},
    booktitle={arXiv:2111.11745},
    year={2021}
}

Contact

If you have any question, please contact mxt_invoker1997@163.com

Carloqiang/DeepRFT