In the animation industry, the colorization of raw sketch images is a vitally important but very time-consuming task. This article focuses on providing a novel solution that semiautomatically colorizes a set of images using a single colorized reference image. Our method is able to provide coherent colors for regions that have similar semantics to those in the reference image. An active-learning-based framework is used to match local regions, followed by mixed-integer quadratic programming (MIQP) which considers the spatial contexts to further refine the matching results. We efficiently utilize user interactions to achieve high accuracy in the final colorized images. Experiments show that our method outperforms the current state-of-the-art deep learning based colorization method in terms of color coherency with the reference image. The region matching framework could potentially be applied to other applications, such as color transfer.

中文翻译：

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卡通线条图的主动着色

在动画行业中，原始草图图像的着色是一项极其重要但非常耗时的任务。本文重点介绍一种新颖的解决方案，该解决方案使用单个着色参考图像对一组图像进行半自动着色。我们的方法能够为与参考图像中语义相似的区域提供连贯的颜色。基于主动学习的框架用于匹配局部区域，然后是混合整数二次规划 (MIQP)，它考虑空间上下文以进一步细化匹配结果。我们有效地利用用户交互来实现最终彩色图像的高精度。实验表明，我们的方法在与参考图像的颜色一致性方面优于当前最先进的基于深度学习的着色方法。