Image Colourization using Generative Adversarial Networks

Abstract Over the last decade, the process of automatic image colourization has been of significant interest for several application areas including restoration of aged or degraded Images. Image colourization is a challenging task in computer vision that involves adding realistic colour to grayscale images. Traditional methods rely on handcrafted rules and heuristics, which can lead to inconsistent and inaccurate results. Generative Adversarial Networks (GANs) offer a promising solution for this problem by learning to generate realistic colour images from grayscale inputs. In this paper, we review the state-of-the-art techniques for image colourization using GANs. We discuss different architectures and loss functions used in GANs for colourization, as well as their advantages and limitations. We also provide an overview of the datasets used for training and evaluation, and the metrics used for measuring the performance of the models. Finally, we discuss the potential applications of GAN-based colourization in various domains, such as art, photography, and medical imaging. Overall, we show that GAN- based approaches offer a powerful and flexible framework for image colourization, with the potential to revolutionize the way we perceive and manipulate visual content.