高光去除的聚类算法改进
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浙江省公益技术研究计划(2017C31032)


Specular Highlight Removal Using Improved Pixel Clustering
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    摘要:

    高光去除是计算机视觉领域研究的一个热点问题.现有的基于双色反色模型分离漫反射分量和镜面反射分量去除单幅图像中的高光的方法,容易引起图像颜色失真和纹理的丢失.针对此问题,在使用像素强度比去高光的基础上改进了像素聚类算法,能够更准确的进行像素分类,改善图像颜色失真的现象.首先计算原图像与最小强度值单通道图像的差值得到无高光图像.然后根据无高光图像计算与高光区域相关的每个像素点的最大漫反射色度值和最小漫反射色度值.最后将高光区域内的像素点转换到最小最大色度空间,对高光区域内的像素点进行x-means聚类,利用分类后漫反射像素点的强度比估计值很容易分离高光区域像素点的镜面反射分量,从而得到去高光图像.实验结果表明,与现有的方法对比,峰值信噪比值平均提升了2%至4%,图像颜色失真和纹理丢失状况得到改善,视觉效果更好.

    Abstract:

    Specular highlight removal is a hot topic in the field of computer vision. Existing methods based on dichromatic reflection model,which separate diffuse and specular reflection components to remove specular highlights in a single image, tend to cause color distortion and texture loss. To relieve this problem, the pixel clustering algorithm is improved by using pixel intensity ratio to remove specular highlight, which can more accurately classify pixels and improve the image color distortion. Firstly, the difference between the original image and the single channel image of the minimum intensity value is calculated, to obtain the specular-free image. Secondly, the minimum and maximum diffuse chromaticity values for each pixel in the highlight area is estimated based on the specular-free image. Finally, the distribution pattern of the pixels in the highlight area are analyzed in a minimum-maximum chromaticity space and clustered by x-means method. The specular components of highlight area pixels can be easily separated by using the estimated intensity ratio of diffuse pixels, thereby getting a non-highlight image. Experimental results show that, compared with the existing method, the peak signal-to-noise ratio increases by 2% to 4% on average, and the image color distortion and texture loss are improved with better visual effects.

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许丽,宋滢.高光去除的聚类算法改进.计算机系统应用,2020,29(1):209-214

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  • 收稿日期:2019-06-04
  • 最后修改日期:2019-06-28
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  • 在线发布日期: 2019-12-30
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