Interactive Anime Sketch Colorization with Style Consistency via a Deep Residual Neural Net

• Main Authors: YE, RU-TING, 葉如婷
• Other Authors: CHUNG, WEN-YU
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/mh8mp4

碩士 === 國立高雄科技大學 === 資訊工程系 === 107 === Anime sketch colorization is to fill the various colors into the anime sketch, to make it colorful and diverse, just like the artist draws. The coloring problem is not a new research direction in the field of deep learning technology, but there are still many people researching related study, anime sketch colorization is one of them. Because coloring of the anime sketch does not have fixed color and also texture or shadow which can be reference information, so it is difficult and complex to let neural network learn how to colorization. Anime sketch coloring problem is divided into automatic coloring and coloring with reference. Automatic coloring is generating coloring results with random color by the network itself. While coloring with reference is the user will first give the network the desired color and where would be coloring or reference colored image, that is the color hint, and then the network will generate coloring results according to the given color hint or reference colored image. After generative adversarial networks (GANS) was proposed, some used GAN to do coloring research, like style2paint to do anime sketch with style image, but also video or photos colorization etc., although achieved some result, the coloring effect is limited. This study proposes a method use deep residual network, and adding discriminator to network, that expect the color of colored images can consistent with the desired color by the user, and can achieve good coloring results.