Awesome
PUGAN_TIP2023
Runmin Cong, Wenyu Yang, Wei Zhang, Chongyi Li, Chun-Le Guo, Qingming Huang, and Sam Kwong, PUGAN: Physical model-guided underwater image enhancement using GAN with dual-discriminators, IEEE Transactions on Image Processing, vol. 32, pp. 4472-4485, 2023.
Network
Our overall framework:
Par-subnet:
TSIE-subnet:
Requirement:
Pleasure configure the environment according to the given version:
- python 3.6.13
- pytorch 1.8.2+cu111
- torchvision 0.9.2
- pillow 8.4.0
- skimage 0.17.2
- numpy 1.19.5
We also provide ".yaml" files for conda environment configuration, you can download it from [Link], code: mvpP, then use conda env create -f requirement.yaml
to create a required environment.
Data Preprocessing
Please follow the tips to download the processed datasets and pre-trained model:
├── utils
├── data_utils.py
├── Par
├── model
├── model.py
├── nets
├── pixpix.py
├── fusion.py
├── commons.py
├── checkpoints
├── test.py
├── train.py
Training and Testing
Training command : Please unzip the training data set to data\input_train and unzip the corresponding reference of training data set to data\gt_train.
We provide "train.yaml" files for training a new model from scratch or from a existing model.
python train.py
You can also train on a UFO or EVUP dataset by modifying train.yaml. We provide download connections for these datasets : UFO: [link], EUVP:[link]
Testing command : Please unzip the testing data set to tests.
We provide "test.yaml" files for testing.
The trained model can be download here: [[Link]n(https://pan.baidu.com/s/1y0_kHl1NRjrKc36LEX9wFQ?pwd=mvpP)], code: .mvpP
python test.py
Evaluation
We implement two metrics: PSNR, MSE.
python evaluations/measure_ssim_psnr.py
Results
- Qualitative results: we provide the saliency maps, you can download them from [Link], code: mvpP.
- Quantitative results:
Bibtex
@article{crm/tip23/PUGAN,
author={Cong, Runmin and Yang, Wenyu and Zhang, Wei and Li, chongyi and Guo, Chun-Le and Huang, Qingming and Kwong, Sam },
journal={IEEE Trans. Image Process. },
title= {{PUGAN}: Physical model-guided underwater image enhancement using {GAN} with dual-discriminators},
volume={32},
pages={4472-4485},
year={2023}
}
Contact Us
If you have any questions, please contact Runmin Cong at rmcong@sdu.edu.cn or Wenyu Yang at wyuyang@bjtu.edu.cn.