Deep Learning-Based Infrared and Visible Image Fusion: A Survey
| Autor: | WANG Enlong, LI Jiawei, LEI Jia, ZHOU Shihua |
|---|---|
| Jazyk: | čínština |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Jisuanji kexue yu tansuo, Vol 18, Iss 4, Pp 899-915 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1673-9418 28240316 |
| DOI: | 10.3778/j.issn.1673-9418.2306061 |
| Popis: | How to preserve the complementary information in multiple images to represent the scene in one image is a challenging topic. Based on this topic, various image fusion methods have been proposed. As an important branch of image fusion, infrared and visible image fusion (IVIF) has a wide range of applications in segmentation, target detection and military reconnaissance fields. In recent years, deep learning has led the development direction of image fusion. Researchers have explored the field of IVIF using deep learning. Relevant experimental work has proven that applying deep learning to achieving IVIF has significant advantages compared with traditional methods. This paper provides a detailed analysis on the advanced algorithms for IVIF based on deep learning. Firstly, this paper reports on the current research status from the aspects of network architecture, method innovation, and limitations. Secondly, this paper introduces the commonly used datasets in IVIF methods and provides the definition of commonly used evaluation metrics in quantitative experiments. Qualitative and quantitative evaluation experiments of fusion and segmentation and fusion efficiency analysis experiments are conducted on some representative methods mentioned in the paper to comprehensively evaluate the performance of the methods. Finally, this paper provides conclusions and prospects for possible future research directions in the field. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|