针对传统Census变换在匹配代价计算中易受噪声影响、匹配精度较低的问题, 提出一种引入噪声容限的四状态Census变换算法。在匹配代价计算中, 首先将改进的Census匹配代价与灰度绝对差值和梯度代价进行融合, 并加入相应的截断阈值, 以提高初始匹配代价空间的可靠性。然后通过引导图滤波进行代价聚合, 并采用赢家通吃策略计算初始视差值。最后通过左右一致性检验、视差填充和加权中值滤波来优化初始视差值, 得到最终视差图。实验结果表明, 所提算法的噪声鲁棒性优于传统Census变换算法, 且立体匹配算法的整体误匹配率降低至5.59%。

Aiming at the problem that traditional Census transform is susceptible to noise and the matching accuracy is low in the matching cost calculation process, a four-state Census transform algorithm with noise tolerance is proposed. In the calculation of the matching cost, firstly, the improved Census matching cost is fused with the matching cost of the absolute difference of grayscale and gradient, and the corresponding cut-off threshold is added to improve the reliability of the initial matching cost volume. Then, the cost aggregation is performed through the guided image filtering, and the strategy of “Winner Takes All” is used to calculate the initial disparity value. Finally, the final disparity map is obtained by optimizing the initial disparity value through left-right consistency checking, disparity filling and weighted median filtering. Experimental results show that the noise robustness of the proposed algorithm is better than that of the traditional Census transform algorithm, and the overall error matching rate of the stereo matching algorithm is reduced to 5.59%.