We propose a novel approach to transferring the color of a reference image to a given source image. Although there can be diverse pairs of source and reference images in terms of content and composition similarity, previous methods are not capable of covering the whole diversity. To resolve this limitation, we propose a deep neural network that leverages color histogram analogy for color transfer. A histogram contains essential color information of an image, and our network utilizes the analogy between the source and reference histograms to modulate the color of the source image with abstract color features of the reference image. In our approach, histogram analogy is exploited basically among the whole images, but it can also be applied to semantically corresponding regions in the case that the source and reference images have similar contents with different compositions. Experimental results show that our approach effectively transfers the reference colors to the source images in a variety of settings. We also demonstrate a few applications of our approach, such as palette-based recolorization, color enhancement, and color editing.

中文翻译：

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使用直方图类比的深色传输

我们提出了一种将参考图像的颜色转移到给定源图像的新方法。尽管在内容和组成相似性方面可以有不同的源图像和参考图像对，但以前的方法不能涵盖整个多样性。为了解决这个限制，我们提出了一种利用颜色直方图类比进行颜色传输的深度神经网络。直方图包含图像的基本颜色信息，我们的网络利用源直方图和参考直方图之间的类比，用参考图像的抽象颜色特征来调制源图像的颜色。在我们的方法中，直方图类比基本上在整个图像中被利用，但它也可以应用于在源图像和参考图像内容相似但构图不同的情况下语义对应的区域。实验结果表明，我们的方法在各种设置下有效地将参考颜色转移到源图像。我们还演示了我们方法的一些应用，例如基于调色板的重新着色、颜色增强和颜色编辑。