融合边缘保持与改进代价聚合的立体匹配算法  

Stereo matching algorithm based on edge preservation and improved cost aggregation

作　　者：程德强[1,2] 李海翔 寇旗旗 于泽宽 庄焕东 吕晨 Cheng Deqiang;Li Haixiang;Kou Qiqi;Yu Zekuan;Zhuang Huandong;Lyu Chen(Engineering Research Center of Intelligent Control for Underground Space,Ministry of Education,China University of Mining and Technology,Xuzhou 221000,China;School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221000,China;School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221000,China;Academy for Engineering and Technology,Fudan University,Shanghai 200433,China)

机构地区：[1]中国矿业大学地下空间智能控制教育部工程研究中心,徐州221000 [2]中国矿业大学信息与控制工程学院,徐州221000 [3]中国矿业大学计算机科学与技术学院,徐州221000 [4]复旦大学工程与应用技术研究院,上海200433

出　　处：《中国图象图形学报》2021年第2期438-451,共14页Journal of Image and Graphics

基　　金：国家自然科学基金项目(51774281)。

摘　　要：目的立体匹配是计算机双目视觉的重要研究方向,主要分为全局匹配算法与局部匹配算法两类。传统的局部立体匹配算法计算复杂度低,可以满足实时性的需要,但是未能充分利用图像的边缘纹理信息,因此在非遮挡、视差不连续区域的匹配精度欠佳。为此,提出了融合边缘保持与改进代价聚合的立体匹配。方法首先利用图像的边缘空间信息构建权重矩阵,与灰度差绝对值和梯度代价进行加权融合,形成新的代价计算方式,同时将边缘区域像素点的权重信息与引导滤波的正则化项相结合,并在多分辨率尺度的框架下进行代价聚合。所得结果经过视差计算,得到初始视差图,再通过左右一致性检测、加权中值滤波等视差优化步骤获得最终的视差图。结果在Middlebury立体匹配平台上进行实验,结果表明,融合边缘权重信息对边缘处像素点的代价量进行了更加有效地区分,能够提升算法在各区域的匹配精度。其中,未加入视差优化步骤的21组扩展图像对的平均误匹配率较改进前减少3.48%,峰值信噪比提升3.57 d B,在标准4幅图中venus上经过视差优化后非遮挡区域的误匹配率仅为0.18%。结论融合边缘保持的多尺度立体匹配算法有效提升了图像在边缘纹理处的匹配精度,进一步降低了非遮挡区域与视差不连续区域的误匹配率。Objective Stereo matching is an important part of the field of binocular stereo vision.It reconstructs 3 D objects or scenes through a pair of 2 D images by simulating the visual system of human beings.Stereo matching is widely used in various fields,such as unmanned vehicles,3 D noncontact measures,and robot navigation.Most stereo matching algorithms can be divided into two types:global and local stereo matching algorithms.A global algorithm obtains a disparity map by minimizing the energy function;it exhibits the advantage of high matching accuracy.However,a global stereo matching algorithm operates with high computational complexity,and it is difficult to apply to some fields that require programs to act fast.Local matching algorithms use only the neighborhood information of pixels in the window to perform pixel-by-pixel matching,and thus,its matching accuracy is lower than that of global algorithms.Local algorithms have lower computational complexity,expanding the application range of stereo matching.Local stereo matching algorithms generally have four steps:cost computation,cost aggregation,disparity computation,and disparity refinement.In cost computation,the cost value of each pixel in the left and right images is computed by the designed algorithm at all disparity levels.The correlation between the pixel to be matched and the candidate pixel is measured using the cost value;a smaller cost value corresponds to higher relevance.In cost aggregation,a local matching algorithm aggregates the cost value within a matching window by summing,averaging,or using other methods to obtain the cumulative cost value to reduce the impact of outliers.The disparity for each pixel is calculated using local optimization methods and refined using different post-processing methods in the last two steps.However,traditional local stereo matching algorithms cannot fully utilize the edge texture information of images.Thus,such algorithms still exhibit poor performance in matching accuracy in non-occluded regions and regions with di

关 键 词：计算机视觉 局部立体匹配 代价计算 边缘保持 引导滤波 

分 类 号：TN911.73[电子电信—通信与信息系统]