fa8106838e
- 重新组织了模型结构，增加了新的特征融合模块 - 添加了深度可分离卷积块和新的细节特征提取模块 - 更新了数据处理流程，使用了新的数据集路径 - 调整了训练参数，增加了训练轮次和学习率- 优化了损失函数，使用了Huber损失替代MSE损失 |
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| .idea | ||
| componets | ||
| image | ||
| logs | ||
| mamba_ssm | ||
| models | ||
| test_img | ||
| test_result | ||
| utils | ||
| .gitignore | ||
| 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}
}