三种测度融合的加权引导滤波匹配算法

A weight guided filtering matching algorithm combining three measures

针对立体匹配算法中,census变换在弱纹理区域具有较好效果,但忽略了图像的灰度信息,造成在重复纹理区域匹配效果不理想,提出了一种改进的census变换。在初始匹配代价阶段,设计了一种在census变换的基础上融合互信息和梯度信息的相似性测度算法。在代价聚合阶段,采用自适应权重引导滤波聚合策略。最后,通过视差计算、视差优化得到最终的视差图。在VS2015软件平台上对Middlebury网站上提供的标准测试图进行实验,实验结果表明,所提算法能够得到较为准确的视差图,平均误匹配率为5.29%,可以满足三维重构的需要。

The census transform in existing stereo matching algorithms has good effect in the weak texture region, but it neglects the gray level information of the image, causing unsatisfactory matching effect in repeated texture regions. We therefore propose an improved census transform. In the initial matching cost stage, we design a similarity measure algorithm based on census transform, mutual information and gradient information. Then in the cost aggregation stage, we adopt an adaptive weight guided filtering aggregation strategy. Finally, the final disparity map is obtained by calculating and optimizing disparity. We use the proposed algorithm to test the standard images provided on the Middlebury website on the VS2015 software platform. Experimental results show that it can get an accurate disparity map and the average mismatch rate is 5.29%, which meets the requirement of 3D reconstruction.