Refined Image Colorization Using Capsule Generative Adversarial Networks

Automatic image colorization has increasingly become a heavily researched topic over the last decade. It has become of interest for many application areas including colorization of black & white movies, historical images, surveillance feeds and generally old image restoration. Recent state-of-the-art methods utilize Deep Convolutional Generative Adversarial Networks (DCGAN) for the colorization process. However, with the introduction of capsule networks, many flaws of the convolutional neural networks began to surface. In this paper, the convolutional network layers inside the discriminator of a DCGAN will be replaced with capsule network layers. Further studies are employed to show how capsule networks as a discriminator in a DCGAN perform in the image colorization task.