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基于形态学变换和FFCM聚类的灰度图像颜色迁移算法

Colour transfer for grayscale images based on morphology transformation and FFCM cluster

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摘要

针对传统颜色迁移算法计算量大、对图像无法准确进行颜色迁移的问题,提出一种基于形态学变换和快速模糊C均值聚类(FFCM)的灰度图像颜色迁移算法。首先对目标图像进行腐蚀膨胀运算,消除亮度不均匀的区域,通过FFCM聚类算法对目标图像进行准确聚类,然后在目标图像与源图像中选取对应样本块,完成样本块的颜色迁移,并以已上色的样本块为参考,完成图像的全局颜色迁移。实验结果表明:与Welsh和FCM算法相比较,本文算法处理时间分别缩短64.29 %和54.25 %,结果图像在类间交界处的颜色过渡更加自然,证明了算法的有效性。

Abstract

To deal with the problem that the traditional color transfer algorithms can not transfer the image colour accurately and have large amount calculation, we proposed a colour transfer algorithm for colorizing grayscale images based on morphology transformation and fast fuzzy C-means (FFCM) cluster. We used morphology transformation to process the target image for removing the non-uniform luminance region first, and adopted the FFCM algorithm to cluster the target image accurately.Then we established the correspondence between the swatches of source and target images to complete the color transfer of swatches which were as the references to complete the target image's colorization.The experimental results showed that, compared with Welsh and fuzzy C-means (FCM),the elapsed time decreased by 64.2% and 54.25% respectively and the result image was more natural in clusters' junction which proved the validity of our algorithm proposed.

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中图分类号:TN911.73

DOI:10.5768/jao201233.0202004

基金项目:国家自然科学基金项目(61171057);山西省自然科学基金项目(2011011015-1);电子测试技术国家重点实验室项目(201003);山西省研究生创新项目(20103082);光电成像技术与系统教育部重点实验室2010年开放基金(2010OEIOF16)

收稿日期:2011-10-12

修改稿日期:2011-10-31

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贺栋:中北大学 信息与通信工程学院,山西 太原 030051
杨风暴:中北大学 信息与通信工程学院,山西 太原 030051
蔺素珍:中北大学 信息与通信工程学院,山西 太原 030051
周萧:中北大学 信息与通信工程学院,山西 太原 030051

联系人作者:贺栋(kjez_001@126.com)

备注:贺栋(1987-),男,山西运城人,硕士研究生,主要从事信号的获取和处理及图像处理。

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引用该论文

HE Dong,YANG Feng-bao,LIN Su-zhen,ZHOU Xiao. Colour transfer for grayscale images based on morphology transformation and FFCM cluster[J]. Journal of Applied Optics, 2012, 33(2): 300-304

贺栋,杨风暴,蔺素珍,周萧. 基于形态学变换和FFCM聚类的灰度图像颜色迁移算法[J]. 应用光学, 2012, 33(2): 300-304

被引情况

【1】耿春明,方菲. 。融合区域生长与霍夫变换的内窥图像分割算法. 应用光学, 2014, 35(6): 1009-1015

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