An Overview of Advances in Image Colorization Using Computer Vision and Deep Learning Techniques

Abstract

Automatic image colorization as a process has been studied extensively over the past 10 years with importance given to its many applications in grayscale image colorization, aged/degraded image restoration etc. In this study, we attempt to trace and consolidate developments made in Image colorization using various computer vision techniques and methodologies, focusing on the emergence and performance of Generative Adversarial Networks (GANs). We talk in depth about GANs and CNNs, namely their structure, functionality and extent of research. Additionally, we explore the advances made in image colorization using other Deep Learning frameworks ranging from LeNets to MobileNets in order of their evolution in detail. We also compare existing published works showcasing new advancements and possibilities, and predominantly emphasize the importance of continuing research in image colorization. We further analyze and discuss potential applications and challenges of GANs to tackle in the future.