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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

IRVIS: This file is an infrared and visible light model file. Its training hyperparameters have been written so you can use them as you wish. link：https://pan.baidu.com/s/1wHqLA3R2ovZyEfTC00wwsg?pwd=6yr2 password：6yr2

CT-MRI: link：https://pan.baidu.com/s/1lrEiVLs2p8cMyCV-GN_ZKQ?pwd=a3v8 password：a3v8

(CT-MRI) Note: You need to modify line 794 in the file FusionMamba/models/vmamba_Fusion_efficross.py by changing {depths=[2, 2, 9, 2], depths_decoder=[2, 9, 2, 2]} to {depths=[2, 2, 1, 2], depths_decoder=[2, 1, 2, 2]}.

4.Train

python train.py

5.Test

python test.py

6.Datasets

KAIST:(https://github.com/SoonminHwang/rgbt-ped-detection)

Link：https://pan.baidu.com/s/1xIlpL21EA7PdFC5PpLviow?pwd=gf0u password：gf0u

medical image fusion data:

This dataset is sourced from the Harvard Public Medical Imaging Collection (https://www.med.harvard.edu/aanlib/home.html), consisting of paired medical images. You may download the original data individually by visiting the official website of the Harvard Public Medical Imaging Database. To facilitate research, we have gathered and processed these paired datasets, which are provided solely for academic research purposes. If you find our processed dataset and this study helpful to your work, we kindly ask that you cite the FusionMamba project in your research. Thank you for your understanding and support.

CT-MRI:

link：https://pan.baidu.com/s/1HyZ48gQtWZNkUZzfoOJYjw?pwd=bbc7 password：bbc7

PET-MRI:

link：https://pan.baidu.com/s/1Cs5fUy4EoI-MtX9DUPRFGA?pwd=blt4 password：blt4

SPECT-MRI:

link：https://pan.baidu.com/s/1EwxfJ7F0SSD157fxdr4XPw?pwd=3mw7 password：3mw7

7.Path

You need to modify the data input path in lines 47-48 of the TaskFusion_dataset.py file. If you intend to train using CT-MRI data, ensure that the structure of the CT-MRI data, after data augmentation, matches the structure of the KAIST dataset. Additionally, replace lwir with CT/PET/SPECT and visible with MRI.

8.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} }