Deep neural networks for automatic image colorization often suffer from the color-bleeding artifact, a problematic color spreading near the boundaries between adjacent objects. Such color-bleeding artifacts debase the reality of generated outputs, limiting the applicability of colorization models in practice. Although previous approaches have attempted to address this problem in an automatic manner, they tend to work only in limited cases where a high contrast of gray-scale values are given in an input image. Alternatively, leveraging user interactions would be a promising approach for solving this color-breeding artifacts. In this paper, we propose a novel edge-enhancing network for the regions of interest via simple user scribbles indicating where to enhance. In addition, our method requires a minimal amount of effort from users for their satisfactory enhancement. Experimental results demonstrate that our interactive edge-enhancing approach effectively improves the color-bleeding artifacts compared to the existing baselines across various datasets.
翻译:用于自动图像色化的深神经网络往往会受到色彩斑点工艺品的影响,这种色彩在相邻物体的边界附近扩散成问题。这种色彩斑点工艺品贬低了生成产出的现实,限制了彩色模型的实际适用性。虽然以前的做法试图以自动方式解决这一问题,但是它们往往只在输入图像中灰度值的高度对比有限的情况下发挥作用。或者,利用用户互动是解决这种颜色斑点工艺品的有希望的办法。在本文中,我们建议通过简单的用户刻字来为感兴趣的区域建立一个新的边际网络,指明要加强的地方。此外,我们的方法需要用户作出最低限度的努力才能令人满意地加强这些产品。实验结果表明,我们互动的边际增强方法能够有效地改进颜色斑点工艺品,而不同数据集的现有基线则可以改进这些颜色斑点工艺品。