Pyramid Attention for Image Restoration

This repository is for PANet and PA-EDSR introduced in the following paper

Yiqun Mei, Yuchen Fan, Yulun Zhang, Jiahui Yu, Yuqian Zhou, Ding Liu, Yun Fu, Thomas S. Huang and Honghui Shi "Pyramid Attention for Image Restoration", [Arxiv]

The code is built on EDSR (PyTorch) & RNAN and tested on Ubuntu 18.04 environment (Python3.6, PyTorch_1.1) with Titan X/1080Ti/V100 GPUs.

Train
Test
Results
Citation
Acknowledgements

Train

Prepare training data

Download DIV2K training data (800 training + 100 validtion images) from DIV2K dataset or SNU_CVLab.
Specify '--dir_data' based on the HR and LR images path.

For more informaiton, please refer to EDSR(PyTorch).

Begin to train

(optional) All the pretrained models and visual results can be downloaded from Google Drive.

Cd to 'PANet-PyTorch/[Task]/code', run the following scripts to train models.

You can use scripts in file 'demo.sb' to train and test models for our paper.

# Example Usage: 
python main.py --n_GPUs 4 --rgb_range 1 --reset --save_models --lr 1e-4 --decay 200-400-600-800 --chop --save_results --n_resblocks 32 --n_feats 256 --res_scale 0.1 --batch_size 16 --model PAEDSR --scale 2 --patch_size 96 --save EDSR_PA_x2 --data_train DIV2K

Test

Quick start

Cd to 'PANet-PyTorch/[Task]/code', run the following scripts.

You can use scripts in file 'demo.sb' to produce results for our paper.

# No self-ensemble, use different testsets to reproduce the results in the paper.
# Example Usage: 
python main.py --model PAEDSR --data_test Set5+Set14+B100+Urban100 --save_results --rgb_range 1 --data_range 801-900 --scale 2 --n_feats 256 --n_resblocks 32 --res_scale 0.1 --pre_train ../model_x2.pt --test_only --chop

The whole test pipeline

Prepare benchmark datasets from SNU_CVLab
Conduct image SR.

See Quick start
Evaluate the results.

Run 'Evaluate_PSNR_SSIM.m' to obtain PSNR/SSIM values for paper.

Citation

If you find the code helpful in your resarch or work, please cite the following papers.

@article{mei2020pyramid,
  title={Pyramid Attention Networks for Image Restoration},
  author={Mei, Yiqun and Fan, Yuchen and Zhang, Yulun and Yu, Jiahui and Zhou, Yuqian and Liu, Ding and Fu, Yun and Huang, Thomas S and Shi, Honghui},
  journal={arXiv preprint arXiv:2004.13824},
  year={2020}
}
@InProceedings{Lim_2017_CVPR_Workshops,
  author = {Lim, Bee and Son, Sanghyun and Kim, Heewon and Nah, Seungjun and Lee, Kyoung Mu},
  title = {Enhanced Deep Residual Networks for Single Image Super-Resolution},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {July},
  year = {2017}
}

Acknowledgements

This code is built on EDSR (PyTorch), RNAN and generative-inpainting-pytorch. We thank the authors for sharing their codes.

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