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Infrared and Visible Image Fusion Technology and Application: A Review

文献类型： 外文期刊

作者： Ma, Weihong 1 ; Wang, Kun 2 ; Li, Jiawei 1 ; Yang, Simon X. 3 ; Li, Junfei 3 ; Song, Lepeng 2 ; Li, Qifeng 1 ;

作者机构： 1.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

2.Chongqing Univ Sci & Technol, Sch Elect Engn, Chongqing 401331, Peoples R China

3.Univ Guelph, Sch Engn, Adv Robot & Intelligent Syst Lab, Guelph, ON N1G 2W1, Canada

关键词： infrared and visible light image; image fusion; evaluation index

期刊名称：SENSORS （ 2022影响因子：3.9； 五年影响因子：4.1 ）

ISSN：

年卷期： 2023 年 23 卷 2 期

页码：

收录情况： SCI

摘要： The images acquired by a single visible light sensor are very susceptible to light conditions, weather changes, and other factors, while the images acquired by a single infrared light sensor generally have poor resolution, low contrast, low signal-to-noise ratio, and blurred visual effects. The fusion of visible and infrared light can avoid the disadvantages of two single sensors and, in fusing the advantages of both sensors, significantly improve the quality of the images. The fusion of infrared and visible images is widely used in agriculture, industry, medicine, and other fields. In this study, firstly, the architecture of mainstream infrared and visible image fusion technology and application was reviewed; secondly, the application status in robot vision, medical imaging, agricultural remote sensing, and industrial defect detection fields was discussed; thirdly, the evaluation indicators of the main image fusion methods were combined into the subjective evaluation and the objective evaluation, the properties of current mainstream technologies were then specifically analyzed and compared, and the outlook for image fusion was assessed; finally, infrared and visible image fusion was summarized. The results show that the definition and efficiency of the fused infrared and visible image had been improved significantly. However, there were still some problems, such as the poor accuracy of the fused image, and irretrievably lost pixels. There is a need to improve the adaptive design of the traditional algorithm parameters, to combine the innovation of the fusion algorithm and the optimization of the neural network, so as to further improve the image fusion accuracy, reduce noise interference, and improve the real-time performance of the algorithm.

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