Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917896827961344 |
|---|---|
| author | Liu, Jinyuan Wu, Guanyao Liu, Zhu Wang, Di Jiang, Zhiying Ma, Long Zhong, Wei Fan, Xin Liu, Risheng |
| author_facet | Liu, Jinyuan Wu, Guanyao Liu, Zhu Wang, Di Jiang, Zhiying Ma, Long Zhong, Wei Fan, Xin Liu, Risheng |
| contents | Infrared-visible image fusion (IVIF) is a critical task in computer vision, aimed at integrating the unique features of both infrared and visible spectra into a unified representation. Since 2018, the field has entered the deep learning era, with an increasing variety of approaches introducing a range of networks and loss functions to enhance visual performance. However, challenges such as data compatibility, perception accuracy, and efficiency remain. Unfortunately, there is a lack of recent comprehensive surveys that address this rapidly expanding domain. This paper fills that gap by providing a thorough survey covering a broad range of topics. We introduce a multi-dimensional framework to elucidate common learning-based IVIF methods, from visual enhancement strategies to data compatibility and task adaptability. We also present a detailed analysis of these approaches, accompanied by a lookup table clarifying their core ideas. Furthermore, we summarize performance comparisons, both quantitatively and qualitatively, focusing on registration, fusion, and subsequent high-level tasks. Beyond technical analysis, we discuss potential future directions and open issues in this area. For further details, visit our GitHub repository: https://github.com/RollingPlain/IVIF_ZOO. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10761 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption Liu, Jinyuan Wu, Guanyao Liu, Zhu Wang, Di Jiang, Zhiying Ma, Long Zhong, Wei Fan, Xin Liu, Risheng Computer Vision and Pattern Recognition Infrared-visible image fusion (IVIF) is a critical task in computer vision, aimed at integrating the unique features of both infrared and visible spectra into a unified representation. Since 2018, the field has entered the deep learning era, with an increasing variety of approaches introducing a range of networks and loss functions to enhance visual performance. However, challenges such as data compatibility, perception accuracy, and efficiency remain. Unfortunately, there is a lack of recent comprehensive surveys that address this rapidly expanding domain. This paper fills that gap by providing a thorough survey covering a broad range of topics. We introduce a multi-dimensional framework to elucidate common learning-based IVIF methods, from visual enhancement strategies to data compatibility and task adaptability. We also present a detailed analysis of these approaches, accompanied by a lookup table clarifying their core ideas. Furthermore, we summarize performance comparisons, both quantitatively and qualitatively, focusing on registration, fusion, and subsequent high-level tasks. Beyond technical analysis, we discuss potential future directions and open issues in this area. For further details, visit our GitHub repository: https://github.com/RollingPlain/IVIF_ZOO. |
| title | Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.10761 |