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FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba

Arxiv| Code |

1. Create Environment

conda create -n FusionMamba python=3.8

conda activate FusionMamba

pip install torch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 --extra-index-url https://download.pytorch.org/whl/cu117 

pip install packaging pip install timm==0.4.12

pip install pytest chardet yacs termcolor

pip install submitit tensorboardX

pip install triton==2.0.0

pip install causal_conv1d==1.0.0 # causal_conv1d-1.0.0+cu118torch1.13cxx11abiFALSE-cp38-cp38-linux_x86_64.whl

pip install mamba_ssm==1.0.1 # mmamba_ssm-1.0.1+cu118torch1.13cxx11abiFALSE-cp38-cp38-linux_x86_64.whl

pip install scikit-learn matplotlib thop h5py SimpleITK scikit-image medpy yacs

2. Prepare Your Dataset

dataset

/dataset/
        set00-setXX/
                        V000-VXXX/
                                        IRimages
                                        VISimages

3. Pretrain Weights

 It ready before September 25th

4.Train

python train.py

5.Test

python test.py

6.Citation

@article{xie2024fusionmamba, title={Fusionmamba: Dynamic feature enhancement for multimodal image fusion with mamba}, author={Xie, Xinyu and Cui, Yawen and Ieong, Chio-In and Tan, Tao and Zhang, Xiaozhi and Zheng, Xubin and Yu, Zitong}, journal={arXiv preprint arXiv:2404.09498}, year={2024} }