摘要
针对现有立体匹配算法对噪声敏感、易失真、在视差不连续区域与弱纹理区域误匹配率高的问题,提出一种改进Census变换与梯度融合的多尺度立体匹配算法。采用支持窗口内所有像素的加权平均灰度值作为Census变换的参考值,将Census代价与由水平和垂直方向归一化结合的梯度代价进行加权融合,通过设置噪声容限获得稳定的代价,提高了单像素匹配代价的可靠性;在多分辨率尺度下,采用改进引导滤波算法完成对匹配代价的聚合;通过视差提取获得视差图。实验结果表明,该算法在Middlebury测试平台上对标准立体图像对的平均误匹配率为4.74%,对27组扩展立体图像对的平均误匹配率为8.67%。该算法使得视差不连续区域与弱纹理区域的误匹配率进一步降低,且对噪声和光照等干扰表现出较好的稳健性。
Aiming at the problems of noise-sensitive,easy distortion and with high false matching ratio in the disparity discontinuity region and weak texture region of the existing local matching algorithm,a multi-scale stereo matching algorithm for improved Census transform and gradient fusion is proposed.The weighted average gray value of all the pixels in the support window is used as the reference value of the Census transform.The Census cost is weighted combined with the gradient cost normalized by the horizontal and vertical directions,and a stable cost is obtained when the noise margin is set.Therefore,the reliability of the single pixel matching cost is obtained.Under the multi-scale,the improved guided filtering algorithm is used to complete the aggregation of the matching cost.The disparity map is obtained by parallax extraction.The experimental results demonstrate that the average false matching ratio of standard stereo image pairs obtained by the proposed algorithm is 4.74% on the Middlebury testing benchmark,and the average false matching ratio of the 27 extended stereo image pairs is 8.67%.In the parallax discontinuity region and the weak texture region,the false matching ratio is further reduced by the proposed algorithm,and it shows better robustness for noise and light.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2018年第2期260-270,共11页
Acta Optica Sinica
基金
河北省自然科学基金(E2016202341)
河北省高等学校科学技术研究项目(BJ2014013)
