# Infrared and Visible Image Fusion: Methods, Datasets, Applications, and Prospects
## 1 Introduction
The blocked abnormal operating area might be found by comparing the visible image's characteristics to those of the infrared image. Multi-sensor fusion was used by Professor [PERSON] to track multiple targets [16]. U.S. engineers created the DVP3000 and DVP4000 systems based on FPGA in 2005, which could combine infrared and visible photos in real-time and be used aboard military ships [17]. The development of visible and infrared imaging seeker technology for surface-to-surface missiles was studied in 2006 by [PERSON] et al. They developed a dual-channel, four-digital signal processor (DSP) image fusion hardware architecture, used fusion technology to recognize military targets in the visible and infrared spectrums, and increased the precision of surface-to-surface missile seeker guidance [18]. The \"Tracker\" strategic reconnaissance vehicle, which can integrate infrared, visible light, laser, and other information to improve the reconnaissance capacity of the reconnaissance vehicle, was created by the United States, the United Kingdom, and others in 2007 [19]. A method of infrared and visible image fusion based on particle swarm optimization for facial image verification was proposed by [PERSON] et al. in 2011 [20]. The \"Resources 1\" satellite, created by China, was successfully launched around the turn of the century. It has an infrared spectrum scanner and a visible CCD camera, both of which China separately created, making a big impression in the field of combining infrared and visible light [21]. An infrared and visible image fusion method based on multi-scale edge-preserving decomposition and guided image filtering was proposed by [PERSON] et al. in 2015 [22]. This method combined the benefits of multi-scale decomposition and guided filtering to effectively suppress artifacts while preserving the details of the source images. In order to address the issues of high maintenance costs and inconvenient management in temperature monitoring in the petroleum industry and other industries, [PERSON] et al. devised an intelligent monitoring system based on infrared and visible light images in 2016 [23]. In order to convert the image fusion problem into a multi-distribution simultaneous estimation problem in 2020 and produce a fused image with significant contrast and rich texture details, [PERSON] et al. [24] proposed GANMcC, a multi-classification constraint generation adduction network for infrared and visible image fusion. In 2021, [PERSON] et al. [25] proposed STDFusionNet, an infrared and visible image fusion network based on salient target recognition that can achieve salient target detection and critical information fusion in an implicit manner. The preservation of thermal targets and background texture is accomplished by guaranteeing the intensity and gradient consistency in specific regions, while the salient target mask is introduced to guide the network to detect salient areas in the first step of our proposal. To solve the issue of insufficient fusion impact of infrared and visible light images in outdoor environments, [PERSON] [26] developed an infrared and visible light outdoor image fusion approach based on the convolutional neural network in July 2022. This method involves preprocessing the input infrared image using a rolling-guided filter. The source image is then transformed into a low-frequency coefficient and a high-frequency coefficient, and the low-frequency coefficient and the high-frequency coefficient are fused using a minimum fusion rule and a fusion rule, respectively. In November 2022, [PERSON] [27] et al. proposed a darkness infrared and visible image fusion method (DIVFusion), which can reasonably illuminate the darkness, encourage the aggregation of complementary information, and address issues with low illumination, texture hiding, and color distortion in visible images taken at night. A distributed fusion architecture for infrared and visible images, called RADFNet, was proposed by [PERSON] [28] et al. in January 2023.