An innovative optimization strategy based on Mamba and generative adversarial networks for efficient and high-performance multimodal image fusion

Introduction

This is official implementation of An innovative optimization strategy based on Mamba and generative adversarial networks for efficient and high-performance multimodal image fusion with Pytorch.

Brightness Enhancement and Texture Detail Preservation

Objective Evaluation and Subjective Visual Assessments Metrics

Framework

Tips

The Trained Model is here.

Recommended Environment

• causal-conv1d 1.1.0
• CUDA 11.8
• conda 4.11.0
• mamba-ssm 1.2.0.post1
• Python 3.7.16
• PyTorch 2.1.1
• timm 1.0.3
• tqdm 4.66.4
• pandas 2.2.2

Citation

If you find this repository useful, please consider citing the following paper:

@article{sun2025EAAI,
  title = {An innovative optimization strategy based on Mamba and generative adversarial networks for efficient and high-performance multimodal image fusion},
  journal = {Engineering Applications of Artificial Intelligence},
  volume = {163},
  pages = {112788},
  year = {2026},
  issn = {0952-1976},
  doi = {https://doi.org/10.1016/j.engappai.2025.112788},
  url = {https://www.sciencedirect.com/science/article/pii/S0952197625028192},
  author = {Yichen Sun and Mingli Dong and Lianqing Zhu},
}

If you have any questions, feel free to contact me (sunyichen0429@163.com)

Acknowledgements

Parts of this code repository is based on the following works:

• https://github.com/state-spaces/mamba
• https://github.com/dongyuya/TGFuse