Name
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1049/cvi2.12096 | IET COMPUTER VISION |
Keywords | DocType | Volume |
colour boundary, generative adversarial network, image colourisation, line art | Journal | 16 |
Issue | ISSN | Citations |
5 | 1751-9632 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dahua Lv | 1 | 0 | 0.34 |
| Yuanyuan Pu | 2 | 0 | 2.37 |
| Rencan Nie | 3 | 0 | 0.34 |