MulFS-CAP: Multimodal Fusion-supervised Cross-modality Alignment Perception for Unregistered Infrared-visible Image Fusion
By Huafeng Li; Zengyi Yang; Yafei Zhang; Wei Jia; Zhengtao Yu; Yu Liu*
Our paper is available online! [IEEE]
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torch 1.12.1
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torchvision 0.13.1
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opencv 4.6.0.66
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kornia 0.5.11
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numpy 1.21.5
1.If you want to test input source images with a fixed resolution of 256x256, you can run following commands:
python test.py
2.If you want to test input source images of arbitrary resolution, you can run following commands:
python test_arbitrary_resolution.py
python train.py
- The pretrained model on the RoadScene dataset is as follows: RoadScene (Google Link)
- If you intend to evaluate the deformed images you constructed, retraining the model is recommended.
@ARTICLE{MulFS-CAP,
author={Li, Huafeng and Yang, Zengyi and Zhang, Yafei and Jia, Wei and Yu, Zhengtao and Liu, Yu},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={MulFS-CAP: Multimodal Fusion-Supervised Cross-Modality Alignment Perception for Unregistered Infrared-Visible Image Fusion},
year={2025},
volume={47},
number={5},
pages={3673-3690},
}