Interactive Colorization via Multi-Cues Manipulation

Interactive Colorization via Multi-Cues Manipulation

Xiaohong Shi (School of Information Science, Xinhua College of Sun Yat-sen University, Guangzhou, China), Xue Yang (National Engineering Research Center of Digital Life, Sun Yat-sen University, Guangzhou, China) and Zhuo Su (School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China)
Copyright: © 2018 |Pages: 12
DOI: 10.4018/IJGHPC.2018040104

Abstract

Colorization plays an important role in image processing, which aims to change the original image color according to specific image color characteristics. In this article, the authors present a regional color editing method based on multi-cues manipulation, including interactive segmentation, inpainting and gradient-preserving optimization. Firstly, the user draws strokes specifying the target region which needs to change color, through which the target image can be segmented by the K-means color clustering method. Then the example-based inpainting technology is applied to achieve natural transition along the boundary. After color propagation, they apply an optimization algorithm to preserve the gradient. The experiment results demonstrate that the proposed approach can not only achieve a visual satisfactory local color propagation results, but also preserve the texture details well.
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Generally speaking, colorization could be roughly classified into two categories--global colorization and local colorization. Both of the two are essential tools for various visual editing task. Global colorization conveys the whole color characteristics of a reference image to a user-specified target image. Reinhard et al. (2001) proposed a global color mapping method mapping a reference image’s color characteristics to another target image using statistical analysis. The color of the reference image is propagated corresponding to the mean and standard deviation of the colors in the target image, keeping the image content naturally. As effective as it is, the quality of colorization relies on the similarity between target and reference images.

Subsequently, lots of complementary methods are proposed to improve Reinhard’s statistical mapping method (Reinhard et al., 2001) to achieve better color stylization results. Chang et al. (2005) proposed a basic color categories-based method that was applied within each pair of convex hull of the same category. Similarly, some approaches of mapping are presented, e.g. Tai et al.’s probabilistic segmentation color transfer (Tai et al., 2005), Wang et al.’s data-driven image color theme enhancement (Wan et al., 2010), Dong et al.’s dominant colors mapping based method (Dong et al., 2010) and Wang et al.’s training based method (Wang et al. 2011).

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