Abstract: Image colorization refers to recovering the color information of an image from a grayscale image. A grayscale image can have multiple reasonable colorization results with multimodal uncertainty. In addition, the problem of color bleeding and dull color often occurs during the colorization. Traditional colorization methods are time-consuming and ineffective. Recently, the application of deep learning techniques has made significant progress in image colorization. This paper classifies natural grayscale image colorization into four categories: scribble-based image colorization, reference-based image colorization, fully automatic image colorization, and text-based image colorization. This paper reviews and summarizes the technical methods of natural image colorization from these four categories, and discusses and analyzes the impact of deep learning on colorization, the loss function and evaluation indicators currently used. Finally, the limitations of current image colorization and future research directions are summarized, which provides references for subsequent image colorization research.