Exemplar-based colorization approaches rely on reference image to provide plausible colors for target gray-scale image. The key and difficulty of exemplar-based colorization is to establish an accurate correspondence between these two images. Previous approaches have attempted to construct such a correspondence but are faced with two obstacles. First, using luminance channels for the calculation of correspondence is inaccurate. Second, the dense correspondence they built introduces wrong matching results and increases the computation burden. To address these two problems, we propose Semantic-Sparse Colorization Network (SSCN) to transfer both the global image style and detailed semantic-related colors to the gray-scale image in a coarse-to-fine manner. Our network can perfectly balance the global and local colors while alleviating the ambiguous matching problem. Experiments show that our method outperforms existing methods in both quantitative and qualitative evaluation and achieves state-of-the-art performance.
翻译:Exmplar基色化方法依靠参考图像为目标灰度图像提供可信的颜色。 实例基色化的关键和困难在于在这两个图像之间建立准确的对应关系。 以前的方法曾试图构建这样的对应关系,但面临两个障碍。 首先,使用光亮渠道计算通信是不准确的。 其次,它们建立的密集通信引入了错误的匹配结果并增加了计算负担。 为了解决这两个问题,我们提议Sermantic-Sparse色化网络(SSCN)将全球图像样式和详细的语义化相关颜色以粗略到精细的方式转换到灰度图像。 我们的网络可以完全平衡全球和本地的颜色,同时缓解模糊的匹配问题。 实验显示,我们的方法在定量和定性评估方面都超越了现有方法,并实现了最先进的性能。