SeAFusion

The code of "Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network"

To Train

Run "CUDA_VISIBLE_DEVICES=0 python train.py" to train your model. The training data are selected from the MFNet dataset. For convenient training, users can download the training dataset from here, in which the extraction code is: bvfl.

To Test

Run "CUDA_VISIBLE_DEVICES=0 python test.py" to test the model.

Recommended Environment

• torch 1.7.1
• torchvision 0.8.2
• numpy 1.19.2
• pillow 8.0.1

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

@article{TANG2022SeAFusion,
title = {Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network},
journal = {Information Fusion},
year = {2022},
issn = {1566-2535},
doi = {https://doi.org/10.1016/j.inffus.2021.12.004},
url = {https://www.sciencedirect.com/science/article/pii/S1566253521002542},
author = {Linfeng Tang and Jiteng Yuan and Jiayi Ma},
}