期刊文献+

场景线稿动漫效果的自动上色算法 被引量:1

Automatic Colorization Algorithm with Anime Effect for Scene Sketches
下载PDF
导出
摘要 场景线稿具有线条语义多样化的特点,直接应用现有的人像线稿图自动上色算法对其着色容易出现上色错误或棋盘效应等结果失真的现象.针对上述问题,文中提出动漫效果自动上色算法.基于条件生成对抗网络,改进和增强人像线稿图自动上色算法中常用的U型网络(U-Net)生成器的结构,设计双层信息抽取的生成器网络(DIEU-Net),自动完成场景线稿到动漫效果的上色.DIEU-Net设计用于抽取场景线稿浅层显著信息的双卷积子模块(IESS).构建双层IESS与残差结构的集成模块,插入生成器的不同阶段,增强网络在与线稿关联的颜色、位置等重要特征上的全域学习能力,缓和网络加深带来的梯度消失等网络退化问题.同时采用“卷积+上采样”操作替换U-Net生成器中原有的反卷积操作,抑制生成结果中棋盘效应的发生.实验表明,文中算法能较好地克服结果失真的问题,上色效果合理、自然,具有较好的应用推广性,可应用于多种类型景物线稿图的动漫上色. When the existing automatic portrait coloring algorithms are directly applied to scene sketches,distortion phenomena are caused,such as wrong colorization and checkerboard artifacts,due to the diversified line semantics of scene sketches.To address this issue,an automatic colorization algorithm with anime effect for scene sketches is put forward.The structure of U-Net generator in the existing automatic portrait coloring algorithms is improved and enhanced based on the conditional generative adversarial network.A double-layer information extraction U-Net(DIEU-Net)is designed for automatic anime effect colorization of scene sketches.Firstly,the double-convolution sub-module prominence-information extraction of a scene sketch(IESS)is designed.Then,a module integrating double-layer IESS and residual structure is inserted into different stages of the proposed generator.Thus,the global learning ability of the generator on important features,like colors and positions related to the sketch,are enhanced,and the network degradation problems caused by vanishing gradients as the network deepens,are alleviated.Moreover,the deconvolution in U-Net is replaced by the operations of convolution and upsample to suppress the occurrence of the checkerboard artifacts.Experimental results show that the proposed algorithm performs well in avoiding the distortion phenomenon and achieves more reasonable and natural coloring effect than other algorithms.Furthermore,the proposed algorithm can be applied to automatic anime coloring of various types of scene sketches.
作者 朱松 陈昭炯 叶东毅 ZHU Song;CHEN Zhaojiong;YE Dongyi(College of Mathematics and Computer Science,Fuzhou University,Fuzhou 350108)
出处 《模式识别与人工智能》 EI CSCD 北大核心 2020年第8期671-680,共10页 Pattern Recognition and Artificial Intelligence
基金 国家自然科学基金项目(No.61672158) 福建省自然科学基金项目(No.2018J01798)资助。
关键词 场景线稿 自动上色 条件生成对抗网络(CGAN) U型网络(U-Net) 残差网络 Scene Sketch Automatic Colorization Condition Generative Adversarial Network(CGAN) U-Net Residual Network
  • 相关文献

参考文献2

二级参考文献26

  • 1孙正兴,冯桂焕,周若鸿.基于草图的人机交互技术研究进展[J].计算机辅助设计与图形学学报,2005,17(9):1889-1899. 被引量:54
  • 2贾云涛,胡事民.基于图切分的交互式图像染色算法[J].计算机学报,2006,29(3):508-512. 被引量:15
  • 3Lieberman H. How to color in a coloring book[CJ // Proceedings of the 5th Annual Conference on Computer Graphics and Interactive Techniques. New York: ACM Press. 1978: 111-116.
  • 4Sykora D. BuridnekJ. ZaraJ. Colorization of black- and-white cartoons[J]. Image and Vision Computing. 2005. 23 (9): 767-782.
  • 5Levin A, Lischinski D, Weiss Y. Colorization using optimization[J]. ACM Transactions on Graphics, 2004. 23 (3): 689-694.
  • 6Yatziv L. Sapiro G. Fast image and video colorization using chrominance blending[J]. IEEE Transactions on Image Processing. 2006. IS(5): 1120-1129.
  • 7Qu Y G. Wong T T. Heng P A. Manga colorization[J]. ACM Transactions on Graphics, 2006. 2S(3): 1214-1220.
  • 8Luan Q, Wen F. Cohen-Or D. et al . Natural image colorization[CJ //Proceedings of Eurographics Symposium on Rendering Techniques. Airc-Ia-Ville , Eurographics Asso-ciation Press, 2007: 309-320.
  • 9Sheng B. Sun H Q, Chen S B. et a. Colorization using the rotation -invariant feature space[J]. IEEE Computer Graphics and Applications. 2011, 31(2): 24-3S.
  • 10Sykora D. DinglianaJ, Collins S. LazyBrush: flexible painting tool for hand-drawn cartoons[J]. Computer Graphics Forum. 2009. 28(2): 599-608.

共引文献13

同被引文献3

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部