ece5f30c2d
- 添加 DynamicFilter 模块，用于频域滤波和动态调整滤波器权重 - 添加 SCSA 模块，探索空间注意力和通道注意力之间的协同作用 - 添加 SMFA 模块，自调制特征聚合用于高效图像重建 - 添加 TIAM 模块，时空交互注意力用于变化检测 - 添加 UFFC 模块，无偏快速傅里叶卷积用于图像修复 - 更新 net.py，引入 SCSA 模块替换原有注意力机制 - 优化 train.py，调整导入模块以支持新功能 |
||
|---|---|---|
| .idea | ||
| componets | ||
| image | ||
| logs | ||
| mamba_ssm | ||
| models | ||
| test_img | ||
| test_result | ||
| utils | ||
| ConvSSM.py | ||
| dataprocessing.py | ||
| net_cddfuse.py | ||
| net_me.py | ||
| net.py | ||
| PFCFuse_IVF.pth | ||
| README.md | ||
| requirement.txt | ||
| status.md | ||
| test_IVF.py | ||
| test_sar.py | ||
| train.py | ||
| trainExe.py | ||
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}
}