摘要
目的由于非均匀光照条件下,物体表面通常出现块状的强反射区域,传统的去高光方法在还原图像时容易造成颜色失真或者边缘的丢失。针对这些缺点,提出一种改进的基于双边滤波的去高光方法。方法首先通过双色反射模型变换得到镜面反射分量与最大漫反射色度之间的转换关系,然后利用阈值将图像的像素点分为两类,将仅含漫反射分量的像素点与含有镜面反射分量的像素点分离开来,对两类像素点的最大漫反射色度分别做估计,接着以估计的最大漫反射色度的相似度作为双边滤波器的值域,同时以图像的最大色度图作为双边滤波的引导图保边去噪,进而达到去除镜面反射分量的目的。结果以经典的高光图像作为处理对象,对含有镜面反射和仅含漫反射的像素点分别做最大漫反射色度估计,再以该估计图作为双边滤波的引导图,不仅能去除镜面反射分量还能有效的保留图像的边缘信息,最大程度的还原图像细节颜色,并且解决了原始算法处理结果中R、G、B三通道相似的像素点所出现的颜色退化问题。用改进的双边滤波去高光算法对50幅含高光的图像做处理,并将该算法与Yang方法和Shen方法分别作对比,结果图的峰值信噪比(PSNR)也分别平均提高4.17%和8.40%,所提算法的处理效果更符合人眼视觉,图像质量更好。结论实验结果表明针对含镜面反射的图像,本文方法能够更有效去除图像的多区域局部高光,完成对图像的复原,可为室内外光照不匀情况下所采集图像的复原提供有效理论基础。
Objective Various inhomogeneous materials, including plastic, wood, ceramic, and other opaque nonconductors with uniform pigmentation, usually present some blocks of reflection areas due to nonuniform illumination conditions. How- ever, these blocks of reflection areas often exert some negative effects on visual quality and degrade the performances of vi- sion algorithms, such as color constancy, image segmentation, visual tracking, and object detection. For simplification, specular components are usually disregarded as outliers by methods based on diffuse component analysis. When we use tra- ditional methods to remove the highlight of a simple image, they cannot completely separate the specular components of the image. For example, the colors and edges of images were always missed, parts of the result image exhibited color distor-tion, caused artifacts for noisy and highly textured surfaces, the method was inapplicable to natural images. In view of these shortcomings, an improved method of removing highlights based on bilateral filtering is proposed. Method To solve the shortcomings of the traditional methods, we raise an improved bilateral filtering method. The method comprises three sta- ges. First, we can determine the relationship between the diffuse component and the maximum diffuse chromaticity of pixels in an image according to the dichromatic reflection model and by assuming that illumination chromaticity can be measured （with a white reference） . If we want to separate the diffuse component from an image, then we must estimate the maxi- mum diffuse chromaticity value for every pixel. An estimation of the maximum diffuse chromaticity, which is the key to sep- arating specular and diffuse components, is thus necessary. Second, we use an appropriate threshold to divide the input im- age into two categories. The first class of pixels contains only a diffuse component, whereas the second class of pixels in- cludes specular and diffuse components. We then implement our proposed method （ two different al
出处
《中国图象图形学报》
CSCD
北大核心
2018年第1期9-17,共9页
Journal of Image and Graphics
基金
河南省自然科学基金项目(162300410126)
河南理工大学博士基金项目(B2010-17)
河南省科技攻关项目(162102210228)
河南省创新型科技人才队伍建设工程(CXTD2016054)~~
关键词
双色反射模型
镜面反射分量
漫反射色度估计
值域
双边滤波
dichromatic reflection model
specular reflection component
the maximum diffuse chromaticity estimation
range
bilateral filtering
