DATFuse: Infrared and Visible Image Fusion via Dual Attention Transformer (IEEE TCSVT 2023)

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.

Comparison with SOTA methods

Fusion results on TNO dataset

Fusion results on RoadScene dataset

Ablation study on network structure

Ablation study on the number of TRMs

Ablation study on the second DARM

Impact of weight parameters in the loss function

Impact of weight parameter α on fusion performance with λ and γ fixed as 100 and 10, respectively.

Impact of weight parameter λ on fusion performance with α and γ fixed as 1 and 10, respectively.

Impact of weight parameter γ on fusion performance with α and λ fixed as 1 and 100, respectively.

Computational efficiency comparisons

Average running time for generating a fused image (Unit: seconds)

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

Cite the paper

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={33},
  number={7},
  pages={3159-3172},
  doi={10.1109/TCSVT.2023.3234340}}

If you have any questions, feel free to contact me (weitang2021@whu.edu.cn).