基于改进Census变换的自适应局部立体匹配

针对现阶段局部立体匹配在弱纹理区域具有匹配精度低且过度依赖中心像素的缺点，提出一种基于改进Census变换的自适应局部立体匹配算法。首先根据中心像素领域的纹理复杂度采用自适应支持窗口改进Census变换，引入Tanimoto系数与Hamming距离算法结合，并融合颜色或亮度差的绝对值用作新的初始匹配代价计算。通过十字交叉域算法进行代价聚合并采用赢家通吃算法计算视差，在视差优化阶段采用左右一致法、迭代投票、插值填充和亚像素细化，针对边缘模糊化将改进的自适应中值滤波用作抑制噪声得到最后的视差图。实验结果表明，本文所提出的算法在Middlebury数据集上的平均误匹配率为4.39%，相较于其他改进的Census变换算法有明显提升，并在抗噪能力上具有一定的鲁棒性和适应性。

Adaptive Local Stereo Matching Based on Improved Census Transform

To address the shortcomings of the current local stereo matching in weak texture regions with low matching accuracy and over-dependence on the central pixel, an adaptive local stereo matching algorithm based on the improved Census transform is proposed. Firstly, the Census transform is improved by using adaptive support window according to the texture complexity of the central pixel domain, introducing Tanimoto coefficients combined with Hamming distance algorithm, and fusing the absolute value of color or luminance difference as the new initial matching cost calculation. The cost aggregation is performed by the cross-cross domain algorithm and the winner-take-all algorithm is used to calculate the parallax. The left-right consistency method, iterative voting, interpolation filling and sub-pixel refinement are used in the parallax optimization stage, and the improved adaptive median filtering is used as noise suppression for edge blurring to obtain the final parallax map. The experimental results show that the proposed algorithm has an average mismatch rate of 4.39% on the Middlebury dataset, which is a significant improvement over other improved Census transform algorithms, and is robust and adaptive in terms of noise immunity.