## 1 Introduction  
Color is an essential information for understanding the world around us, as human eyes are significantly more sensitive to color than to black and white. Therefore, color images provide us with more abundant visual information. Image colorization refers to assigning new colors to each pixel of a grayscale image. Nowadays, image colorization has been extensively applied in various fields, such as artistic creation [1], remote sensing [2, 3], medical imaging [4], comics [5], infrared imaging [6], etc. Infrared radiation is emitted by objects and can be captured by infrared cameras, producing infrared images. Infrared imaging technology originated in the military field, where it was used to manufacture a series of military products such as aiming devices and guidance heads. With the development and maturity of infrared imaging technology, the application scenarios of infrared imageshave gradually expanded. Infrared cameras can be found in agriculture [7], medicine [8], electricity [9], and other fields. Infrared thermal imaging technology can be used for various types of detection, such as equipment failure detection and material defect detection [10], and is widely used in the steel industry. Infrared imaging captures information that is invisible to the human eye, which makes it valuable. However, infrared images captured at night are grayscale images that lack accuracy and details, limiting their interpretability and usability, and causing great difficulties in post-processing. To address the problem of background residue or target defect in the detection of infrared dark targets in complex marine environments [11], infrared image colorization can be used to separate targets from the background, thereby improving detection accuracy. Due to the potential of infrared images in the aforementioned fields of application, there has been a great deal of attention paid to infrared image colorization. There are two main categories of traditional colorization methods: user-assisted techniques based on manual painting and reference example-based methods. The former method relies on manually applying partial colorization to the image, which can be subject to human subjective bias and ultimately affect the quality of the coloring results. The latter relies heavily on the reference image used, and using an inappropriate example can lead to colorization that is grossly mismatched with the actual colors in the image. Additionally, the complexity and unique characteristics of infrared images make it difficult for these methods to fully leverage the information within the image, resulting in low-quality colorization results. Therefore, these two traditional methods are not well-suited for the task of infrared image colorization. In recent years, deep learning has been widely applied in the field of computer vision. This approach has greatly solved the problem of complex human-machine system interaction processes that exist in the first two methods. By training machines to autonomously complete the coloring task, the application of deep neural networks to image coloring problems has also received good feedback [12; 13; 14; 15]. Colorizing infrared images presents unique challenges compared to colorizing grayscale images due to the need to estimate both luminance and chromaticity. [PERSON] et al. [16] proposed two different methods to estimate color information for infrared images, with the first method estimating both luminance and chromaticity and the second method predicting chromaticity using grayscale image colorization after predicting the luminance. However, the lack of corresponding datasets for NIR and RGB images limited evaluations to traffic scenes. [PERSON] et al. [17] addressed this issue by using unsupervised learning with CycleGAN to generate corresponding images, but the approach suffered from distortion issues. [PERSON] et al.