Color and Grayscale Pencil Image Translation Into Realistic Images Using Generative Adversarial Networks

This work presents an innovative approach to image-to-image translation, focusing on converting colour pencil images, grayscale pencil images, and grayscale images into RGB images using the Pix2Pix GAN. Building upon the established Pix2Pix framework, which utilizes a U-Net based architecture with skip connections for the generator and a Patch-GAN discriminator. This aims to refine the translation process, addressing the images’ intricate details and colour dynamics. Empirical results demonstrate that this generator architecture significantly outper- forms the U-Net architecture in producing more vivid, detailed and accurate RGB images. By leveraging this method, the study not only contributes to the body of knowledge in neural network applications for artistic image translation but also opens new avenues for further researches in image translations. This work underscores the potential of targeted architectural modifications within GAN frameworks to achieve image translation outcomes, thereby setting a precedent for future explorations in the field.