Image Fusion Evaluation

📰 News

• [2024-08-12] Open-sourcing evaluation code with 21 metrics for infrared and visible image fusion!

🗼 Platform

• Matlab

🚩 Structure of Folder

Dataset Folder
    ├─21_pairs_tno
    │  ├─ccfuse
    │  │    Fuse1.png
    │  │    Fuse10.png
    │  │    ......
    │  │
    │  ├─ir
    │  │    IR1.png
    │  │    IR10.png
    │  │    ......
    │  │
    │  └─vis
    │       VIS1.png
    │       VIS10.png
    │       ......     
    │
    ├─40_vot_tno
    │  ├─ir
    │  │    IR1.png
    │  │    IR11.png
    │  │    ......
    │  │
    │  └─vis
    │       VIS1.png
    │       VIS11.png
    │       ......
    │       
    └─output
        └─21_pairs_tno
            └─ccfuse
                ├─evaluation_metrics
                │      all_results.txt
                │
                └─evaluation_metrics_single
                        Fuse1.txt
                        Fuse10.txt
                        ......
                        output_single.xlsx

• As shown above, 21_pairs_tno and 40_vot_tno are the folders of the dataset, and output is the result after running evaluation.
• Take the dataset 21_pairs_tno as an example. Folder ir holds the infrared images, referring to the format of "IR1.png". Folder vis holds the infrared images, referring to the format of "VIS1.png". Folder ccfuse holds the fused results, which name of refers to "Fuse1.png".
• ".png", ".jpg" and '.bmp' are all allowed to use.
• Folder output classifies the data first by dataset and then by algorithm. Evaluation_metrics holds the average of all fused images, and evaluation_metrics_single holds fused images separately.

💁 Get Started

• Git clone the repository.
• Prepare the data as the structure of folder.
• Get to the project of top folder.
• Change the default path in amain.m

vifb_path = "datasetexample\"; % better to use an absolute path
bench = "21_pairs_tno";
method = "ccfuse";

🖼️ Metrics

• Entropy(EN)
• Cross Entropy(CE)
• Mutual Information(MI)
• FMI_pixel
• FMI_dct
• FMI_w
• Peak signal to noise ratio(PSNR)
• MS structural similarity(MS-SSIM)
• Root mean square error(RMSE)
• Spaial Frequency(SF)
• Standard deviation(SD)
• Variance
• Edge Intensity(EI)
• Average gradient(AG)
• VIF
• Qcb
• Gradient based similarity measurement(Qabf)
• Correlation coefficient(CC)
• Sum of correlation differences(SCD)
• Nabf
• Qcv

📈 Star Rising

📋 Citation

Thanks to Linfeng Tang and Chenzhang Xing for the open source code, please cite these papers if you are using this code.

@article{Tang2022Survey,
  title={Deep learning-based image fusion: A survey},
  author={Tang, Linfeng and Zhang, Hao and Xu, Han and Ma, Jiayi},  
  journal={Journal of Image and Graphics}
  volume={28},
  number={1},
  pages={3--36},
  year={2023}
}


@article{Tang2022SuperFusion,
  title={SuperFusion: A versatile image registration and fusion network with semantic awareness},
  author={Tang, Linfeng and Deng, Yuxin and Ma, Yong and Huang, Jun and Ma, Jiayi},
  journal={IEEE/CAA Journal of Automatica Sinica},
  volume={9},
  number={12},
  pages={2121--2137},
  year={2022},
  publisher={IEEE}
}


@article{Ma2022SwinFusion,
  title={SwinFusion: Cross-domain Long-range Learning for General Image Fusion via Swin Transformer},
  author={Ma, Jiayi and Tang, Linfeng and Fan, Fan and Huang, Jun and Mei, Xiaoguang and Ma, Yong},
  journal={IEEE/CAA Journal of Automatica Sinica},
  volume={9},
  number={7},
  pages={1200--1217},
  year={2022},
  publisher={IEEE}
}


@article{TangSeAFusion,
title = {Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network},
author = {Linfeng Tang and Jiteng Yuan and Jiayi Ma},
journal = {Information Fusion},
volume = {82},
pages = {28-42},
year = {2022},
issn = {1566-2535},
publisher={Elsevier}
}


@article{Tang2022DIVFusion,
  title={DIVFusion: Darkness-free infrared and visible image fusion},
  author={Tang, Linfeng and Xiang, Xinyu and Zhang, Hao and Gong, Meiqi and Ma, Jiayi},
  journal={Information Fusion},
  volume = {91},
  pages = {477-493},
  year = {2023},
  publisher={Elsevier}
}


@article{Tang2022PIAFusion,
  title={PIAFusion: A progressive infrared and visible image fusion network based on illumination aware},
  author={Tang, Linfeng and Yuan, Jiteng and Zhang, Hao and Jiang, Xingyu and Ma, Jiayi},
  journal={Information Fusion},
  volume = {83-84},
  pages = {79-92},
  year = {2022},
  issn = {1566-2535},
  publisher={Elsevier}
}


@article{Ma2021STDFusionNet,
  title={STDFusionNet: An Infrared and Visible Image Fusion Network Based on Salient Target Detection},
  author={Jiayi Ma, Linfeng Tang, Meilong Xu, Hao Zhang, and Guobao Xiao},
  journal={IEEE Transactions on Instrumentation and Measurement},
  year={2021},
  volume={70},
  number={},
  pages={1-13},
  doi={10.1109/TIM.2021.3075747}，
  publisher={IEEE}
}



@inproceedings{zhang2020vifb,
title={VIFB: A Visible and Infrared Image Fusion Benchmark},
author={Zhang, Xingchen and Ye, Ping and Xiao, Gang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year={2020}}  


@article{zhang2023visible,
title={Visible and Infrared Image Fusion Using Deep Learning},
author={Zhang, Xingchen and Demiris, Yiannis},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2023},
publisher={IEEE}}