The source code of our CAE-GAN will be released when the paper is accepted by the journal of Engineering Applications of Artificial Intelligence.
We recommended the following dependencies.
- Python 3.7
- PyTorch 1.7.1
- tqdm 4.42.1
- munch 2.5.0
- torchvision 0.8.2
Prepare the training, testing, and validation data. The folder structure should be:
data
└─── fiveK
├─── train
| ├─── exp
| | ├──── a1.png
| | └──── ......
| └─── raw
| ├──── b1.png
| └──── ......
├─── val
| ├─── label
| | ├──── c1.png
| | └──── ......
| └─── raw
| ├──── c1.png
| └──── ......
└─── test
├─── label
| ├──── d1.png
| └──── ......
└─── raw
├──── d1.png
└──── ......
raw/
contains low-quality images, exp/
contains unpaired high-quality images, and label/
contains corresponding ground truth.
To train CAE-GAN on FiveK, run the training script below.
python main.py --mode train --version CAE-GAN-FiveK --use_tensorboard False \
--is_test_nima True --is_test_psnr_ssim True
To test CAE-GAN on FiveK, run the test script below.
python main.py --mode test --version CAE-GAN-FiveK --pretrained_model xx (best epoch, e.g., 100) \
--is_test_nima True --is_test_psnr_ssim True