DDcGAN-tensorflow:
infrared and visible image fusion via dual-discriminator conditional generative adversarial network
This work can be applied for
- multi-resolution infrard and visible image fusion
- same-resolution infrared and visible image fusion
- PET and MRI image fusion
Framework:
Generator architecture:
Training dataset:
- vis-ir dataset (password:nh2r).
- PET-MRI dataset (password: 5d9y).
The code to create your own training dataset can be found here.
If this work is helpful to you, please cite it as:
@article{ma2020ddcgan,
title={DDcGAN: A Dual-Discriminator Conditional Generative Adversarial Network for Multi-Resolution Image Fusion},
author={Ma, Jiayi and Xu, Han and Jiang, Junjun and Mei, Xiaoguang and Zhang, Xiao-Ping},
journal={IEEE Transactions on Image Processing},
volume={29},
pages={4980--4995},
year={2020},
publisher={IEEE}
}s
The previous version of our work can be seen in this paper:
@inproceedings{xu2019learning,
title={Learning a generative model for fusing infrared and visible images via conditional generative adversarial network with dual discriminators},
author={Xu, Han and Liang, Pengwei and Yu, Wei and Jiang, Junjun and Ma, Jiayi},
booktitle={proceedings of Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19)},
pages={3954--3960},
year={2019}
}
This code is base on the code of DenseFuse.
If you have any question, please email to me ([email protected]).