Corruptive Artifacts Suppression for Example-Based Color Transfer

Corruptive Artifacts Suppression for Example-Based Color Transfer
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基于示例的颜色传输的损坏伪影抑制

DOI:
10.1109/tmm.2014.2305914
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发表时间:
2014-06
影响因子:
7.3
通讯作者:
Xiaonan Luo
Xiaonan Luo
中科院分区:
计算机科学1区
文献类型:
--
作者:
Kun Zeng;Li Liu;Bo Li;Xiaonan Luo

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基于实例的颜色传递是图像编辑中的一个关键操作,但在映射过程中容易受到一些破坏性伪影的影响。在本文中,我们提出了一种新的统一的颜色传输框架与腐败文物的抑制,执行迭代概率颜色映射与自学习过滤方案和多尺度细节操作方案,以最小化归一化Kullback-Leibler距离。首先,应用迭代概率颜色映射来构建参考图像和目标图像之间的映射关系。然后,一个自学习的过滤方案被应用到转移过程中,以防止伪影和提取细节。传输的输出和提取的多层次细节通过测量最小化进行整合,以产生最终结果。我们的框架实现了声音颗粒抑制,颜色保真度和细节外观无缝。为了演示,一系列的客观和主观的测量被用来评估在颜色转移的质量。最后,通过几个扩展应用实例验证了该框架的适用性.
Example-based color transfer is a critical operation in image editing but easily suffers from some corruptive artifacts in the mapping process. In this paper, we propose a novel unified color transfer framework with corruptive artifacts suppression, which performs iterative probabilistic color mapping with self-learning filtering scheme and multiscale detail manipulation scheme in minimizing the normalized Kullback-Leibler distance. First, an iterative probabilistic color mapping is applied to construct the mapping relationship between the reference and target images. Then, a self-learning filtering scheme is applied into the transfer process to prevent from artifacts and extract details. The transferred output and the extracted multi-levels details are integrated by the measurement minimization to yield the final result. Our framework achieves a sound grain suppression, color fidelity and detail appearance seamlessly. For demonstration, a series of objective and subjective measurements are used to evaluate the quality in color transfer. Finally, a few extended applications are implemented to show the applicability of this framework.
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