SFCFusion: Spatial–Frequency Collaborative Infrared and Visible Image Fusion

PyTorch implementation of SFCFusion, from the following paper: SFCFusion

News:

• [2024.3.27] Release source code.
• [2024.3.7] Paper published on IEEE Transactions on Instrumentation and Measurement.
• [2023.8.6] Release Inference code for infrared-visible image feep fusion.

Installation:

Evaluation

Our env is:

Python: 3.10.12
Pytorch: 1.13

Evaluation step:

1. Run main.m in MATLAB.
2. Run SFCFusionDeepfuse\main.py in python.
3. Edit the parameter deep in nsst_fuse.m from 0 to 1.
4. Run main.m in MATLAB. The final output is in fused folder.

Training

Please use the train.py in SFCFusionDeepfuse folder.

Acknowledgement

This work is inspired by Densefuse

Citation

If you find this repository helpful, please consider citing:

@ARTICLE{10445750,
  author={Chen, Hanrui and Deng, Lei and Chen, Zhixiang and Liu, Chenhua and Zhu, Lianqing and Dong, Mingli and Lu, Xitian and Guo, Chentong},
  journal={IEEE Transactions on Instrumentation and Measurement}, 
  title={SFCFusion: Spatial–Frequency Collaborative Infrared and Visible Image Fusion}, 
  year={2024},
  volume={73},
  number={},
  pages={1-15},
  keywords={Frequency-domain analysis;Image fusion;Feature extraction;Image reconstruction;Fuses;Collaboration;Semantics;Deep learning;image fusion;multiscale transformation (MST);spatial-frequency;visible-infrared image},
  doi={10.1109/TIM.2024.3370752}}

License

This project is released under the Apache License 2.0.