Cartoon Image Processing: A Survey
Cartoon Image Processing: A Survey
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DOI：
10.1007/s11263-022-01645-1
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发表时间：
2022-09
影响因子：
19.5
通讯作者：
Yangshen Zhao;Diya Ren;Yuan Chen;Wei Jia;Ronggang Wang;Xiaoping Liu
中科院分区：
文献类型：
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作者：
Yangshen Zhao;Diya Ren;Yuan Chen;Wei Jia;Ronggang Wang;Xiaoping Liu
随着动漫产业的快速发展，针对不同的应用场景，如质量评估、风格转换、彩色化、检测、压缩、生成和编辑等，对二维动漫的研究也层出不穷。然而，目前还缺乏文献对这些二维卡通图像处理(CIP)作品进行全面的总结和介绍。卡通图像通常由清晰的线条、流畅的色块和平坦的背景组成，这与自然图像有很大的不同。因此，根据动画片的特点，提出了许多具体的CIP策略。特别是随着深度学习技术的发展，最近的CIP方法已经取得了比直接应用自然图像处理算法更好的结果。因此，本文根据不同的场景和应用，对二维CIP方法的共性和差异性进行了综述，并着重介绍了近年来基于深度学习的算法。此外，本文还收集了相关的CIP数据集，对一些典型的任务进行了实验，并对未来的工作进行了讨论。
With the rapid development of cartoon industry, various studies on two-dimensional (2D) cartoon have been proposed for different application scenarios, such as quality assessment, style transfer, colorization, detection, compression, generation and editing. However, there is still a lack of literature to summarize and introduce these 2D cartoon image processing (CIP) works comprehensively. The cartoon images are usually composed of clear lines, smooth color patches and flat backgrounds, which are quite different from natural images. Therefore, based on the characteristics of cartoons, many specific CIP strategies are proposed. Especially with the development of deep learning technology, recent CIP methods have achieved better results than direct application of natural image processing algorithms. Thus, this paper reviews the commonalities and differences of 2D CIP methods according to different scenarios and applications, and focuses on recent deep-learning-based algorithms specifically. In addition, this paper also collects related CIP datasets, conducts experiments for some typical tasks, and discusses the future work.