This is official Pytorch implementation of "A Full-Scale Hierarchical Encoder-Decoder Network with Cascading Edge-prior for Infrared and Visible Image Fusion"
@article{luo2023full,
title={A Full-Scale Hierarchical Encoder-Decoder Network with Cascading Edge-prior for Infrared and Visible Image Fusion},
author={Luo, Xiaoqing and Wang, Juan and Zhang, Zhancheng and Wu, Xiao-jun},
journal={Pattern Recognition},
pages={110192},
year={2023},
publisher={Elsevier}
}
The overall framework of the proposed FSFusion.
- Downloading the pre-trained checkpoint from hed_pretrained_bsds.caffemodel and putting it in
./HED
. - The pre-trained checkpoint is put in
./models/Final.model
. - The test datasets are put in
./images/
. - The result data_root are put in
./outputs/dataset_name
.
Then running test.py