Lab分通道直方图的彩色图像分割算法及应用

本文针对传统灰度直方图分割法未综合考虑图像色度及纹理特征、对灰度差异不明显或灰度范围重叠的图像出现过分割或欠分割等问题,提出一种新的基于Lab分通道直方图的彩色图像分割算法,引入具有序列不相关性的亮度L通道、红绿a通道及蓝黄b通道3种分割依据,通过Newton插值法进行拟合运算,可针对不同亮度、色度属性图像进行自由选择,并运用邻域灰度值相匹配原则解决相邻目标区域边缘像素的准确匹配问题,分局部、分形态、分区域实现图像中不同目标的提取。经验证,该法对区域亮度差异较大图像及区域色度差异显著于亮度差异图像的分割效果,均优于传统灰度直方图分割法,极大提升了直方图分割算法的适用性。将其与经典Reinhard色彩迁移算法结合,将源图像感兴趣目标区域经分通道分割后分别进行色彩迁移变换,较好解决了经典Reinhard算法对图像非目标区域的干扰、色彩误传及阶调层次损失严重等问题,突破传统迁移算法只能整体着色的局限性,实现分区域精准着色。

Color Image Segmentation Algorithm Based on Lab Sub Channel Histogram and Its Application

In view of the color and texture features of the image are not taken into account in the traditional gray scale histogram segmentation algorithm, this paper proposed a new color image segmentation algorithm based on Lab sub channel histogram to solve the problems such as over segmentation or under segmentation for no obvious gray value difference images or grayscale overlapped images. The algorithm introduces three segmentation bases with sequence irrelevance:luminance "L" channel, red-green "a" channel and blue-yellow "b" channel, and then use the Newton interpolation method to fit operation. Users can choose the channel freely for different brightness and chromaticity attribute images. The paper also uses the matching principle of adjacent area gray value to solve the exact matching problem of pixels in the adjacent target area. The algorithm realizes the extraction of different objects in the image by sub office, sub morphology and subarea. In the light of images with significant difference in regional luminance characteristics or regional color difference greater than regional brightness difference, the segmentation results of the algorithm in this paper are better than the traditional gray scale histogram segmentation algorithm through a series of experimental verification, it greatly improves the applicability of histogram segmentation algorithm. Then we combine the new algorithm with the classic Reinhard color transfer algorithm, use the sub channel segmentation algorithm to obtain the target regions of interest from the source image and then transfer them separately. It solves problems of the interference to non-target area of image, color miscommunication and serious level loss of classical Reinhard color transfer algorithm. It breaks through the limitation that the traditional transfer algorithm can only coloring the whole region, and realizes accurate coloring in the subarea.