This is the official implementation of the DATFuse model proposed in the paper (DATFuse: Infrared and Visible Image Fusion via Dual Attention Transformer) with Pytorch.
Method | TNO Dataset | RoadScene Dataset |
---|---|---|
MDLatLRR | 26.0727 | 11.7310 |
AUIF | 0.1119 | 0.0726 |
DenseFuse | 0.5663 | 0.3190 |
FusionGAN | 2.6796 | 1.1442 |
GANMcC | 5.6752 | 2.3813 |
RFN_Nest | 2.3096 | 0.9423 |
CSF | 10.3311 | 5.5395 |
MFEIF | 0.0793 | 0.0494 |
PPTFusion | 1.4150 | 0.8656 |
SwinFuse | 3.2687 | 1.6478 |
DATFuse | 0.0257 | 0.0141 |
If this work is helpful to you, please cite it as:
@ARTICLE{Tang_2023_DATFuse,
author={Tang, Wei and He, Fazhi and Liu, Yu and Duan, Yansong and Si, Tongzhen},
journal={IEEE Transactions on Circuits and Systems for Video Technology},
title={DATFuse: Infrared and Visible Image Fusion via Dual Attention Transformer},
year={2023},
volume={},
number={},
pages={1-15},
doi={10.1109/TCSVT.2023.3234340}}
If you have any questions, feel free to contact me ([email protected]).