LineGAN: An image colourisation method combined with a line art network

Abstract

Abstract The work on grayscale image colourisation has been significantly improved. Currently, learning‐based methods have achieved some great colourisation effects, but existing colour edge bleeding, especially when colourful cartoon characters. In this paper, we focus on the colourisation of cartoon characters from a series in an adversarial environment with a line art network, whose name is LineGAN. LineGAN learns the corresponding colour mapping from datasets, improving the accuracy of image colourisation. Our methods limit the colour boundary overflow by adding a line art frame in the generator. Extensive experiment results on cartoon image colourisation tasks demonstrate that the proposed method can achieve effective results.

Keywords

• colour boundary
• generative adversarial network
• image colourisation
• line art