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Zhaozixiang1228 2023-07-18 09:43:42 +02:00
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@ -32,11 +32,11 @@ Multi-modality (MM) image fusion aims to render fused images that maintain the m
## Usage ## Usage
### Network Architecture ### ⚙ Network Architecture
Our CDDFuse is implemented in ``net.py``. Our CDDFuse is implemented in ``net.py``.
### Training ### 🏊 Training
**1. Virtual Environment** **1. Virtual Environment**
``` ```
# create virtual environment # create virtual environment
@ -67,7 +67,7 @@ python train.py
``` ```
and the trained model is available in ``'./models/'``. and the trained model is available in ``'./models/'``.
### Testing ### 🏄 Testing
**1. Pretrained models** **1. Pretrained models**
@ -136,7 +136,7 @@ CDDFuse_MIF 3.9 58.31 20.87 2.49 1.35 0.97 0.78 1.48
``` ```
which can match the results in Table 5 in our original paper. which can match the results in Table 5 in our original paper.
## CDDFuse ## 🙌 CDDFuse
### Illustration of our CDDFuse model. ### Illustration of our CDDFuse model.
@ -169,7 +169,7 @@ MM segmentation
<img src="image//MMSeg.png" width="60%" align=center /> <img src="image//MMSeg.png" width="60%" align=center />
## Related Work ## 📖 Related Work
- Zixiang Zhao, Haowen Bai, Jiangshe Zhang, Yulun Zhang, Kai Zhang, Shuang Xu, Dongdong Chen, Radu Timofte, Luc Van Gool. *Equivariant Multi-Modality Image Fusion.* **arXiv:2305.11443**, https://arxiv.org/abs/2305.11443 - Zixiang Zhao, Haowen Bai, Jiangshe Zhang, Yulun Zhang, Kai Zhang, Shuang Xu, Dongdong Chen, Radu Timofte, Luc Van Gool. *Equivariant Multi-Modality Image Fusion.* **arXiv:2305.11443**, https://arxiv.org/abs/2305.11443