PFCFuse: A Poolformer and CNN fusion network for Infrared-Visible Image Fusion

The implementation of our paper "PFCFuse: A Poolformer and CNN fusion network for Infrared-Visible Image Fusion".

Recommended Environment:

python=3.8
torch=1.12.1+cu113
scipy=1.9.3
scikit-image=0.19.2
scikit-learn=1.1.3
tqdm=4.62.0

Network Architecture:

Our PFCFuse is implemented in net.py.

Training:

Data preprocessing

Run

python dataprocessing.py

Model training

Run

python train.py

Testing:

Run

python test_IVF.py

相关工作

@inproceedings{zhao2023cddfuse,
  title={Cddfuse: Correlation-driven dual-branch feature decomposition for multi-modality image fusion},
  author={Zhao, Zixiang and Bai, Haowen and Zhang, Jiangshe and Zhang, Yulun and Xu, Shuang and Lin, Zudi and Timofte, Radu and Van Gool, Luc},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={5906--5916},
  year={2023}
}