Deep image colorization networks often suffer from the color-bleeding artifact, a problematic color spreading near the boundaries between adjacent objects. The color-bleeding artifacts debase the reality of generated outputs, limiting the applicability of colorization models on a practical application. Although previous approaches have tackled this problem in an automatic manner, they often generate imperfect outputs because their enhancements are available only in limited cases, such as having a high contrast of gray-scale value in an input image. Instead, leveraging user interactions would be a promising approach, since it can help the edge correction in the desired regions. In this paper, we propose a novel edge-enhancing framework for the regions of interest, by utilizing user scribbles that indicate where to enhance. Our method requires minimal user effort to obtain satisfactory enhancements. Experimental results on various datasets demonstrate that our interactive approach has outstanding performance in improving color-bleeding artifacts against the existing baselines.

本文提出了一种基于用户交互的边缘增强网络，通过简单的用户涂鸦指示增强哪些感兴趣区域，实现对色彩渗透问题的有效改善，且只需要用户进行最小的努力。实验结果表明，与现有基线相比，我们的交互式边缘增强方法在各种数据集上有效地改善了色彩渗透问题。

深度边缘感知交互式彩色渲染 抵御颜色渗透影响

深度边缘感知交互式彩色渲染抵御颜色渗透影响