2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDTEC-3.2
Paper Title PAINTING STYLE-AWARE MANGA COLORIZATION BASED ON GENERATIVE ADVERSARIAL NETWORKS
Authors Yugo Shimizu, Tokyo University of Science, Japan; Ryosuke Furuta, University of Tokyo, Japan; Delong Ouyang, Yukinobu Taniguchi, Tokyo University of Science, Japan; Ryota Hinami, Shonosuke Ishiwatari, Mantra Inc., Japan
SessionTEC-3: Restoration and Enhancement 3
LocationArea G
Session Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Time:Wednesday, 22 September, 08:00 - 09:30
Presentation Poster
Topic Image and Video Processing: Restoration and enhancement
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Abstract Japanese comics (called manga) are traditionally created in monochrome format. In recent years, in addition to monochrome comics, full color comics, a more attractive medium, have appeared. Unfortunately, color comics require manual colorization, which incurs high labor costs. Although automatic colorization methods have been recently proposed, most of them are designed for illustrations, not for comics. Unlike illustrations, since comics are composed of many consecutive images, the painting style must be consistent. To realize consistent colorization, we propose here a semi-automatic colorization method based on generative adversarial networks (GAN); the method learns the painting style of a specific comic from small amount of training data. The proposed method takes a pair of a screen tone image and a flat colored image as input, and outputs a colorized image. Experiments show that the proposed method achieves better performance than the existing alternatives.