基于形态学变换和FFCM聚类的灰度图像颜色迁移算法

 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。

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

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.