摘要
目的 线稿上色是由线条构成的黑白线稿草图涂上颜色变为彩色图像的过程,在卡通动画制作和艺术绘画等领域中是非常关键的步骤。全自动线稿上色方法可以减轻绘制过程中烦琐耗时的手工上色的工作量,然而自动理解线稿中的稀疏线条并选取合适的颜色仍较为困难。方法 依据现实场景中特定绘画类型常有固定用色风格偏好这一先验,本文聚焦于有限色彩空间下的线稿自动上色,通过约束色彩空间,不仅可以降低语义理解的难度,还可以避免不合理的用色。具体地,本文提出一种两阶段线稿自动上色方法。在第1阶段,设计一个灰度图生成器,对输入的稀疏线稿补充线条和细节,以生成稠密像素的灰度图像。在第2阶段,首先设计色彩推理模块,从输入的颜色先验中推理得到适合该线稿的色彩子空间,再提出一种多尺度的渐进融合颜色信息的生成网络以逐步生成高质量的彩色图像。结果 实验在3个数据集上与4种线稿自动上色方法进行对比,在上色结果的客观质量对比中,所提方法取得了更高的峰值信噪比(peak signal to noise ratio,PSNR)和结构相似性(structural similarity index measure,SSIM)值以及更低的均方误差;在上色结果的色彩指标对比中,所提方法取得了最高的色彩丰富度分数;在主观评价和用户调查中,所提方法也取得了与人的主观审美感受更一致的结果。此外,消融实验结果也表明了本文所使用的模型结构及色彩空间限制有益于上色性能的提升。结论 实验结果表明,本文提出的有限色彩空间下的线稿自动上色方法可以有效地完成多类线稿的自动上色,并且可以简单地通过调整颜色先验以获得更多样的彩色图像。
Objective In the art field,an exquisite painting typically takes considerable effort from sketch drawing in the early stage to coloring and polishing.With the rise of animation,painting,graphic design,and other related industries,sketch colorization has become one of the most tedious and repetitive processes.Although some computer-aided design tools have appeared in past decades,they still require humans to accomplish colorization operations,and drawing an exquisite painting is difficult for ordinary users.Meanwhile,automatic sketch colorization is still a difficult problem.Therefore,both the academia and industry are in urgent need of convenient and efficient sketch colorization methods.With the development of deep neural networks(DNNs),DNN-based colorization methods have achieved promising performance in recent years.However,most studies have focused on grayscale image colorization for natural images,which is quite different from sketch colorization.At present,only a few studies have focused on automatic sketch colorization,and they typically require user guidance or are designed for certain types,such as anime characters.However,automatically understanding sparse lines and selecting appropriate colors remain extremely difficult and ill-posed problems.Thus,disharmonious colors frequently appear in recent automatic sketch colorization results,e.g.,red grass and black sun.Therefore most sketch colorization methods reduce difficulty through user guidance,which can be roughly divided into three types:reference image-based,color hints-based,and text expression-based.Although these semi-automatic methods can provide more reasonable results,they still require inefficient user interaction processes.Method In practice,we observe the phenomenon that colors used in paintings of a particular style are typically fixed and finite,rather than arbitrary colorization.Therefore,this study focuses on automatic sketch colorization with finite color space prior,which can effectively reduce the difficulty of understanding semantics
作者
陈缘
赵洋
张效娟
刘晓平
Chen Yuan;Zhao Yang;Zhang Xiaojuan;Liu Xiaoping(School of internet,anhui university,hefei 230039,China;School of computer science and information engineering hefei universityof technology,hefei 230601,China;School of computer,qinghai normal university,xining 810008,China)
出处
《中国图象图形学报》
CSCD
北大核心
2024年第4期978-988,共11页
Journal of Image and Graphics
基金
青海省重点研发与转化计划(2021-GX-111)
国家自然科学基金项目(62262056,61972129)。
关键词
线稿上色
有限色彩空间
卡通
绘画
生成对抗网络
sketch colorization
finite color space
cartoon
painting
generative adversarial network
