Luminance-Chrominance Model for Image Colorization
Luminance-Chrominance Model for Image Colorization
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DOI：
10.1137/140979368
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发表时间：
2015-03
期刊：
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通讯作者：
F. Pierre;Jean-François Aujol;Aurélie Bugeau;N. Papadakis;Vinh-Thong Ta
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文献类型：
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作者：
F. Pierre;Jean-François Aujol;Aurélie Bugeau;N. Papadakis;Vinh-Thong Ta
提出了一种灰度图像上色的新方法。虽然亮度通道的计算是直接通过线性变换来完成的，但着色过程是一个需要一定先验的不适定问题。在文献中存在两类方法。第一类包括手动方法，这些方法需要用户手动在图像上添加颜色以进行着色。第二类包括基于示例的方法，其中将具有类似语义内容的彩色图像作为输入提供给该方法。这两种类型的先验都有各自的优点和缺点。本文提出了一种新的基于实例的着色变分框架。采用非局部方法在源图像中寻找相关颜色，以建议灰度图像上的颜色。结果的空间一致性以及最终的颜色选择由基于总变分的非凸变分框架提供。给出了一种有效的原对偶算法，并证明了其收敛性。在这项工作中，我们还扩展了提出的基于示例的方法，以结合基于示例的方法和手动方法。它提供了统一两种方法优点的单一框架。最后，通过实验和与最先进的方法的比较，说明了我们的建议的有效性。1. 介绍。灰度图像的着色包括向其添加颜色信息。在娱乐行业，让老作品更有吸引力是很有用的。反向操作是基于感知假设的，目前是一个活跃的研究领域[28]，[13],[37]。着色也可以用来添加信息，以帮助用户进一步分析图像（例如，传感器融合[43]）。它也可以用于艺术品修复；看，例如[17]或[41]。这是一个古老的主题，始于屏幕和设备显示颜色的能力。一种开创性的方法是将每个灰度级别映射到色彩空间[18]中。然而，如果没有额外的预处理，所有的颜色都不能恢复。在现有的方法中，先验的添加有两种方式：一种是直接添加颜色
This paper provides a new method to colorize gray-scale images. While the computation of the luminance channel is directly performed by a linear transformation, the colorization process is an ill-posed problem that requires some priors. In the literature two classes of approach exist. The first class includes manual methods that need the user to manually add colors on the image to colorize. The second class includes exemplar-based approaches where a color image, with a similar semantic content, is provided as input to the method. These two types of priors have their own advantages and drawbacks. In this paper, a new variational framework for exemplar-based colorization is proposed. A nonlocal approach is used to find relevant color in the source image in order to suggest colors on the gray-scale image. The spatial coherency of the result as well as the final color selection is provided by a nonconvex variational framework based on a total variation. An efficient primal-dual algorithm is provided, and a proof of its convergence is proposed. In this work, we also extend the proposed exemplar-based approach to combine both exemplar-based and manual methods. It provides a single framework that unifies advantages of both approaches. Finally, experiments and comparisons with state-of-the-art methods illustrate the efficiency of our proposal. 1. Introduction. The colorization of a gray-scale image consists of adding color information to it. It is useful in the entertainment industry to make old productions more attractive. The reverse operation is based on perceptual assumptions and is today an active research area [28], [13], [37]. Colorization can also be used to add information in order to help further analysis of the image by a user (e.g., sensor fusion [43]). It can also be used for art restoration ; see, e.g., [17] or [41]. It is an old subject that began with the ability of screens and devices to display colors. A seminal approach consists in mapping each level of gray into a color-space [18]. Nevertheless, all colors cannot be recovered without an additional prior. In the existing approaches, priors can be added in two ways: with a direct addition of color on