Automatic Colorization

Automatic image colorization aims to intelligently assign colors to grayscale images, a challenging inverse problem with multiple valid solutions. Recent research focuses on improving color accuracy and realism using various deep learning architectures, including Generative Adversarial Networks (GANs), diffusion models, and transformers, often incorporating additional information like textual descriptions, audio, or exemplar images to guide the colorization process. These advancements are impacting fields like animation, art restoration, and even astronomical image processing by enabling efficient and high-quality colorization of diverse image types. The development of new evaluation metrics, such as Chromatic Number Ratio, also contributes to a more rigorous assessment of colorization performance.