SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Images

Zhishe Wang,Yanlin Chen,Wenyu Shao,Hui Li,Lei Zhang

paper

Platform

Python 3.7

Pytorch >=1.6.0

Training Dataset

MS-COCO 2014 (T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, and C. L. Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 3-5.) is utilized to train our auto-encoder network.

Tips:

Large files should be downloaded separately, including the following files:

For testing:

• trained model
  

Extraction code: mn9k

Ciation

If this work is helpful to you, please cite it as:

@ARTICLE{9832006
  author={Wang, Zhishe and Chen, Yanlin and Shao, Wenyu and Li, Hui and Zhang, Lei},
  journal={IEEE Transactions on Instrumentation and Measurement}, 
  title={SwinFuse: A Residual Swin Transformer Fusion Network for Infrared and Visible Images}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TIM.2022.3191664}}

If you have any question, please email to me (wangzs@tyust.edu.cn).