Open AccessLineGAN: An image colourisation method combined with a line art network
Author(s) -
Lv Dahua,
Pu Yuanyuan,
Nie Rencan
Publication year - 2022
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/cvi2.12096
Subject(s) - computer science , artificial intelligence , grayscale , generator (circuit theory) , image (mathematics) , focus (optics) , computer vision , line (geometry) , frame (networking) , limit (mathematics) , mathematics , power (physics) , telecommunications , mathematical analysis , physics , geometry , quantum mechanics , optics
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.