A2N
This repository is an PyTorch implementation of the paper
"Attention in Attention Network for Image Super-Resolution" [arXiv]
Visual results in the paper are availble at Google Drive or Baidu Netdisk (password: 7t74).
Unofficial TensorFlow implementation: https://github.com/Anuj040/superres
Test
Dependecies: PyTorch==0.4.1 (Will be updated to support PyTorch>1.0 in the future)
You can download the test sets from Google Drive. Put the test data in ../Data/benchmark/
.
python main.py --scale 4 --data_test Set5 --pre_train ./experiment/model/aan_x4.pt --chop --test_only
If you use CPU, please add "--cpu".
Train
Training data preparation
- Download DIV2K training data from DIV2K dataset or SNU_CVLab.
- Specify
'--dir_data'
in option.py based on the data path.
For more informaiton, please refer to EDSR(PyTorch).
Training
# SR x2
python main.py --scale 2 --patch_size 128 --reset --chop --batch_size 32 --lr 5e-4
# SR x3
python main.py --scale 3 --patch_size 192 --reset --chop --batch_size 32 --lr 5e-4
# SR x4
python main.py --scale 4 --patch_size 256 --reset --chop --batch_size 32 --lr 5e-4
Citation
If you have any question or suggestion, welcome to email me at here.
If you find our work helpful in your resarch or work, please cite the following papers.
@misc{chen2021attention,
title={Attention in Attention Network for Image Super-Resolution},
author={Haoyu Chen and Jinjin Gu and Zhi Zhang},
year={2021},
eprint={2104.09497},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
Acknowledgements
This code is built on EDSR (PyTorch) and PAN. We thank the authors for sharing their codes.