基于改进Census变换和自适应支持域的立体匹配

Stereo Matching Based on Improved Census Transformation and Adaptive Support Region

为了提升局部立体匹配的精度,提出了一种基于改进Census变换和自适应支持域的立体匹配算法。针对传统Census变换算法对中心点处的采样敏感、误匹配率高的问题,结合中心点左右插值点的信息,提出了一种对采样不敏感的改进Census变换算法。在计算匹配代价阶段,将改进Census变换与彩色信息及x、y方向的梯度信息相融合以构建匹配代价;在代价聚合阶段,提出了基于改进引导滤波的十字交叉法以构建自适应支持域并聚合代价;最后采用赢者通吃(WTA)策略计算视差,并通过多步细化得到最终视差图。实验结果表明,所提算法在Middlebury测试平台4组标准图像上的平均误匹配率为4.92%,具有较高的精度和较好的适应性。

In order to improve the accuracy of local stereo matching,a stereo matching algorithm is proposed,which is based on improved Census transformation and adaptive support region.To solve the problem that the traditional Census transformation algorithm is sensitive to the sampling at a center point and has a high mismatching rate,this paper proposes an improved Census transformation algorithm which is insensitive to sampling by combining the information of the interpolation points at the left and right of a center point.In the stage of matching cost calculation,the improved Census transformation is combined with the color information and gradient information in xand y directions to construct the matching cost.In the stage of cost aggregation,a cross-based approach based on improved guided filtering is proposed to construct adaptive support regions and aggregate costs.Finally,the WTA strategy is used to calculate disparity,and the final disparity map is obtained through a multi-step refinement.The experimental results show that the algorithm proposed here has an average mismatch rate of 4.92%in four sets of standard images on the Middlebury test platform,indicating that it has high accuracy and good adaptability.