文献类型：期刊文献

英文题名：A Review of Infrared and Visible Light Image Fusion Algorithms

作者：Yao, Jilin[1]; Zhang, Cheng[1,2]; Liu, Hongzhe[1]; Xu, Bingxin[1]; Pan, Weiguo[1]; Dai, Songyin[1]; Xu, Cheng[1,2]

第一作者：Yao, Jilin

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China; [2] Beijing Qiangqiang Yuanqi Technology Co., Ltd., Beijing, 100101, China

第一机构：北京联合大学北京市信息服务工程重点实验室

年份：2026

卷号：2816 CCIS

起止页码：120-131

外文期刊名：Communications in Computer and Information Science

收录：EI(收录号：20263421327582)

语种：英文

外文关键词：Advanced driver assistance systems - Automobile drivers - Autonomous vehicles - Convolutional neural networks - Deep neural networks - Feature extraction - Image enhancement - Infrared imaging - Learning algorithms - Optimization

摘要：With the advancement of autonomous driving technology, the integration of infrared and visible light imagery has emerged as a critical component for advanced driver-assistance systems (ADAS) and autonomous driving applications. Infrared imagery facilitates robust target detection under low-light conditions and adverse weather environments, whereas visible light imagery offers detailed textural information. The integration of these two categories of images significantly enhances environmental perception in terms of precision and robustness. This paper systematically reviews recent methodologies for infrared and visible light image fusion within the context of driving assistance, emphasizing deep learning-based approaches such as autoencoders, convolutional neural networks, generative adversarial networks, Transformer architectures, and emerging multimodal foundation models. In contrast to conventional image fusion methods grounded in mathematical models, deep learning-based approaches demonstrate superior performance in managing complex scenarios involving multiple targets, owing to their capacity for automatic feature extraction and fusion strategy optimization. The paper further assesses fusion metrics, presents widely-used datasets (e.g., LLVIP, RoadScene, and M3FD), underscores the constraints of existing methodologies, and proposes future research trajectories for multimodal and foundation-model-driven fusion paradigms. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.