UIDNet in Tensorflow
The code for the AAAI-20 paper End-to-End Unpaired Image Denoising with Conditional Adversarial Networks (Aminer version).
Prerequisites
- Python 3.6
- Tensorflow 1.4.0
- Keras 2.2.4
- SciPy 1.1.0
Usage
To train a model on Low-dose CT images:
$ python main.py --ndct ./data/CUT_3mm/cut_clean/ --ldct ./data/CUT_1mm/cut_noise/ --model_name wgan-gp --c_dim 1 --epoch 50 --gpu_id 0
To test with an existing model:
$ python main.py --ndct ./data/test_1mm/cut_clean/ --ldct ./data/test_1mm/cut_noise/ --model_name wgan-gp --c_dim 1 --epoch 50 --gpu_id 0 --is_train False --checkpoint_dir ckpt_95002
You can refer to DCGAN-tensorflow for more examples.
Results of Low-dose CT image denoising
Citation
If you use this code for your research, please cite our papers.
@inproceedings{hong2020end,
title = {End-to-End Unpaired Image Denoising with Conditional Adversarial Networks},
author = {Zhiwei Hong and Xiaocheng Fan and Tao Jiang and Jianxing Feng,
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
year = {2020},
}