LDRepFM: A Real-time End-to-End Visible and Infrared Image Fusion Model Based on Layer Decomposition and Re-parameterization
- python 3.8
- torch 1.11.0
- torchvision 0.12.0
Pre-training weights have been uploaded
Ours-training python fuse.py --mode train
Ours-inference python fuse.py --mode deploy
Firstly, you need to download the M3FD dataset.
Secondly, clone the folders into our repository as
data
├── train
| ├── M3FD
| | ├──Annotations
| | | ├── 01863.xml
| | | └── ...
| | ├──ir
| | | ├── 01863.png
| | | └── ...
| | ├──vi
| | | ├── 01863.png
| | | └── ...
| | ├──la
| | | ├── 01863.png
| | | └── ...
| | ├──lb
| | | ├── 01863.png
| | | └── ...
└── ...
Thirdly, run python lab_xml2png.py --mode pm
to generate mask images in the la folders.
Fourthly, run python lab_xml2png.py --mode mm
to generate mask images in the lb folders.
Moreover, if you want to view the mask images under the la or lb folders. We provide a method using MATLAB. Of course, you can also use other methods.
I = imread('./data/train/M3FD/la/00000.png');
imagesc(I);
Finally, run python train.py --gpus -1
to train LDRepFM.
If you have any other questions about the code, please email minglu@stu.jiangnan.edu.cn
If this work has been helpful to you, please feel free to cite our paper!
@ARTICLE{10138238,
author={Lu, Ming and Jiang, Min and Kong, Jun and Tao, Xuefeng},
journal={IEEE Transactions on Instrumentation and Measurement},
title={LDRepFM: A Real-Time End-to-End Visible and Infrared Image Fusion Model Based on Layer Decomposition and Re-Parameterization},
year={2023},
volume={72},
number={},
pages={1-12},
doi={10.1109/TIM.2023.3280496}}