GAN-based image colourisation with feature reconstruction loss

Abstract

Automatic image colourisation is a complex and ambiguous task due to having multiple correct solutions. Previous approaches have resulted in desaturated results unless relying on a significant user interaction. In this thesis we study the state of the art for colourisation and we propose an automatic colourisation approaches based on generative adversarial networks that incorporates a feature reconstruction loss during training. The generative network is framed in an adversarial model that learns how to colourise by incorporating perceptual understanding of the colour. Qualitative and quantitative results show the capacity of the proposed method to colourise images in a realistic way, boosting the colourfulness and perceptual realism of previous GAN-based methodologies. We also study and propose a second approach that incorporates segmentation information in the GAN framework and obtain quantitative and qualitative results.