The code of "Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network"
The paper is now freely available for download through the following links: https://authors.elsevier.com/c/1eMto5a7-Gls0k.
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.
The MFNet dataset can be downloaded via the following link: https://drive.google.com/drive/folders/18BQFWRfhXzSuMloUmtiBRFrr6NSrf8Fw.
The MFNet project address is: https://www.mi.t.u-tokyo.ac.jp/static/projects/mil_multispectral/.
Run **CUDA_VISIBLE_DEVICES=0 python test.py** to test the model.
For quantitative assessments, please follow the instruction to modify and run . /Evaluation/test_evaluation.m .
- torch 1.7.1
- torchvision 0.8.2
- numpy 1.19.2
- pillow 8.0.1
@article{TANG202228SeAFusion,
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},
volume = {82},
pages = {28-42},
year = {2022},
issn = {1566-2535}
}