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Attention in Attention Network for Image Super-Resolution (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.

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