Infrared imaging technology converts invisible thermal radiation into visible images, thus extending the human eye’s spectral range from the visible spectrum (380?780 nm) to long-wave infrared (up to 14 μm), making it possible for the human eye to see clearly at night. This technology has been widely used in security monitoring, military reconnaissance, and intelligent driving. However, thermal infrared images are usually presented in grayscale, and their inherent limitations include blurred edges, low contrast, and poor detail expression. In addition, grayscale images only provide limited brightness information and cannot match the long-term color perception preference of the human visual system. This inconsistency often leads to visual fatigue and reduces the accuracy of target recognition by the human eye. In addition, the limitations of grayscale image information content hinder the direct migration of scene perception models trained based on visible spectrum color images to infrared applications. Infrared image colorization technology enriches image color and structural information, improves the accuracy of night target recognition, and the observer’s visual comfort. Its potential impact is comparable to the historical transition from black and white television to color television, marking an important leap in infrared imaging technology.

The development of infrared image colorization technology has experienced a transition from pseudo-color to natural color, which are divided into two categories: traditional methods and deep learning methods (Fig. 1). Traditional infrared image colorization methods mainly include pseudo-color methods based on preset mapping, methods based on color transfer, and methods based on multi-spectral fusion. Pseudo-color methods based on preset mapping can simply realize infrared image colorization, but their colors are unnatural (Fig. 3). The color transfer-based method improves the naturalness of the colorized image by selecting a color reference image to color the corresponding semantic target (Fig. 6), but it requires manual participation, and the colorization effect is heavily dependent on the selection of the reference image, lacking adaptability to complex scenes. The colorization method based on multi-spectral fusion generates pseudo-color images using spectral information of different spectral bands. Although it improves the infrared image colorization effect (Fig. 7), it requires multiple detectors to collect data simultaneously, which greatly increases the complexity of the system. In recent years, with the development of deep learning, new solutions have been provided for infrared image colorization. Deep learning-based infrared image colorization methods are mainly divided into two categories: convolutional neural networks and generative adversarial networks. Table 1 summarizes infrared image colorization methods based on deep learning. The convolutional neural network-based method achieves automatic conversion from infrared to color images by learning feature representations in large-scale paired datasets. However, it is difficult for convolutional neural network models to learn complex image distributions, resulting in a lack of authenticity in the generated images. The generative adversarial network-based method generates colorized images that are closer to the labeled image by introducing adversarial training. Although the colorization methods based on deep learning have improved the quality of colorized infrared images, there are still problems such as color distortion in low-contrast areas, limited generalization ability in diverse infrared scenes, and high model computational cost.

Infrared image colorization technology has narrowed the visual gap between infrared imaging and visible imaging. This paper reviews various methods and their basic principles based on the development history of infrared image colorization technology. Traditional colorization methods rely on prior knowledge or auxiliary images, which makes it difficult to achieve adaptive alignment with semantic information, and their application scope is limited. In recent years, deep learning technology has brought new opportunities for automatic colorization of infrared images, especially GANs have shown significant advantages in enhancing image quality. However, existing deep learning methods still face many challenges. On the one hand, complex networks increase the computational cost and porting difficulty of the colorization model; on the other hand, the performance of the colorization model is constrained by the training data set, and the generalization to multiple scenes is insufficient. Therefore, there is still room for further research on infrared image colorization technology based on deep learning. At the same time, in future research, physical priors (such as the law of thermal radiation) should be combined with deep learning models to integrate multi-source spectral information to improve the robustness of the colorization model. In addition, it is necessary to build a lightweight network architecture to realize the real-time deployment of infrared image colorization models on embedded systems, and to promote the application of infrared imaging technology in practical scenarios such as security monitoring and intelligent driving.