Latest News 🔥🔥

[2024-12-12] Our survey paper [Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption.] has been accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence! (Paper)(中文版)

[2026-04-15] We have updated the repository with state-of-the-art methods for both Image Fusion and Video Fusion.

IVIF Zoo

Welcome to IVIF Zoo, a comprehensive repository dedicated to Infrared and Visible Image Fusion (IVIF). Based on our survey paper [Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption. Jinyuan Liu, Guanyao Wu, Zhu Liu, Di Wang, Zhiying Jiang, Long Ma, Wei Zhong, Xin Fan, Risheng Liu*], this repository aims to serve as a central hub for researchers, engineers, and enthusiasts in the field of IVIF. Here, you'll find a wide array of resources, tools, and datasets, curated to accelerate advancements and foster collaboration in infrared-visible image fusion technologies.

_{A detailed spectrogram depicting almost all wavelength and frequency ranges, particularly expanding the range of the human visual system and annotating corresponding computer vision and image fusion datasets.}

The diagram of infrared and visible image fusion for practical applications. Existing image fusion methods majorly focus on the design of architectures and training strategies for visual enhancement, few considering the adaptation for downstream visual perception tasks. Additionally, from the data compatibility perspective, pixel misalignment and adversarial attacks of image fusion are two major challenges. Additionally, integrating comprehensive semantic information for tasks like semantic segmentation, object detection, and salient object detection remains underexplored, posing a critical obstacle in image fusion.

A classification sankey diagram containing typical fusion methods.

导航(Navigation)

• 数据集 (Datasets)
• 方法集 (Method Set)
• 纯融合方法 (Fusion for Visual Enhancement)
• 数据兼容方法 (Data Compatible)
• 面向应用方法 (Application-oriented)
• 评价指标 (Evaluation Metric)

🔥🚀资源库 (Resource Library)

It covers all results of our survey paper, available for download from Baidu Cloud. - 💥融合 (Fusion) - ✂️分割 (Segmentation) Based on SegFormer - 🔍检测 (Detection) Based on YOLO-v5 - 计算效率 (Computational Efficiency)

数据集(Datasets)

图像数据集（Image Datasets）

视频数据集（Video Datasets）

If the M³FD and FMB datasets are helpful to you, please cite the following paper:

@inproceedings{liu2022target,
  title={Target-aware dual adversarial learning and a multi-scenario multi-modality benchmark to fuse infrared and visible for object detection},
  author={Liu, Jinyuan and Fan, Xin and Huang, Zhanbo and Wu, Guanyao and Liu, Risheng and Zhong, Wei and Luo, Zhongxuan},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={5802--5811},
  year={2022}
}

@inproceedings{liu2023multi,
  title={Multi-interactive feature learning and a full-time multi-modality benchmark for image fusion and segmentation},
  author={Liu, Jinyuan and Liu, Zhu and Wu, Guanyao and Ma, Long and Liu, Risheng and Zhong, Wei and Luo, Zhongxuan and Fan, Xin},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={8115--8124},
  year={2023}
}

方法集(Method Set)

纯融合方法(Fusion for Visual Enhancement)