The code of "CAMF: An Interpretable Infrared and Visible Image Fusion Network Based on Class Activation Mapping"

requirement:

tensorflow-gpu 1.15
opencv-python
Pillow
scipy 1.2.1

Test:

Download the pre-trained checkpoint from here and put them in ./checkpoint/

Run python test_one_image.py to test. The test_dataset can be set as 'tno', 'roadscene' or 'medical'.

Train:

The auto-encoder is trained using the MS-COCO dataset.

You can download my training set from here or download the original data from the official website.

Run python train_auto_encoder.py to train the auto-encoder network.

Run python train_classifier.py to train the classifier network.

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

@article{tang2023camf,
  title={CAMF: An Interpretable Infrared and Visible Image Fusion Network Based on Class Activation Mapping},
  author={Tang, Linfeng and Chen, Ziang and Huang, Jun and Ma, Jiayi},
  journal={IEEE Transactions on Multimedia},
  year={2023},
  publisher={IEEE}
}