基于改进SGM的立木视差图生成方法

Generating Disparity Image of Standing Trees Based on Improved SGM

立木视差图是立木因子测量、三维重建的基础。结合立木图像特征,为解决自然环境下立木图像结构复杂、光照干扰大等因素导致获取高质量立木视差图困难的问题,提出一种基于改进的semiglobal matching(SGM)算法的立木视差图生成方法。针对SGM算法在图像纹理较弱和光照不稳定时生成的视差图效果不佳的问题,提出改进Census变换,该变换将Census中心像素值用周围像素的中值替代,提高初始代价的可靠性;在代价聚合过程中使用均值漂移算法进行图像分割,使算法具有较强鲁棒性的同时还有效降低了对重复和弱纹理区域的误匹配率。最后,分别采用自适应窗口填充无效值、中值滤波剔除不可靠视差值,使视差不连续的区域也能获得准确的视差值。在Middlebury公共数据集上对所提方法进行验证,所提方法的平均误匹配率约为5.23%,较传统的semiglobal block matching(SGBM)算法、BoyerMoore(BM)算法、SGM算法,分别提升9.47个百分点、9.345个百分点、8.96个百分点。自然环境下,所提改进的SGM算法可生成较高精确度的立木视差图。

A standing tree disparity image is the basis of tree factor measurement and 3D reconstruction.However,one challenge is its difficulty in obtaining highquality standing tree disparity image due to the complex structure of standing tree images and large illumination interference in the natural environment.Combined with the characteristics of standing tree images,in this paper,we propose a method for generating a standing tree disparity image using improved semiglobal matching(SGM)algorithm.To solve the problem of poor disparity image generated using the SGM algorithm when the image texture and illuminations are weak and unstable,respectively,we employ an improved Census transform to replace the Census center pixel value with the median of the surrounding pixels to improve the reliability of the initial cost.Furthermore,the mean shift algorithm is used for image segmentation in the process of cost aggregation to enhance the robustness of the algorithm and effectively reduce the false matching rate for repeated and weak texture regions.Finally,we adopt the adaptive window to fill in invalid values and apply a median filter to eliminate unreliable parallax values,so that the area with discontinuous disparity can also obtain accurate disparity value.The proposed method was verified on the Middlebury public dataset.The results show that the average mismatch rate of the proposed method is approximately 5.23%,compared with the traditional semiglobal block matching(SGBM),BoyerMoore(BM),and SGM algorithms with improvements of 9.47 percentage points,9.345 percentage points,and 8.96 percentage points,respectively.In the natural environment,the proposed SGM algorithm can be used to generate a standing tree disparity image with higher accuracy.