Noise robust image matching using adjacent evaluation census transform and wavelet edge joint bilateral filter in stereo vision

Abstract

Automation application systems based on stereo vision require robust image matching methods to achieve available depth image information. This paper presents a novel noise robust stereo matching using adjacent evaluation census transform and wavelet edge joint bilateral filter. The adjacent evaluation census is firstly proposed to improve the robustness against noise of the census transform. Meanwhile, two different and complementary types of metrics are extracted (the adjacent evaluation census mean and the adjacent evaluation census weighted difference). Moreover, the weighted template is composed of four different directions. Then, to improve the robustness of cost aggregation and disparity optimization, the random walk is integrated into the proposed stereo matching method. Additionally, a disparity map post-processing method named wavelet edge joint bilateral filter is employed to eliminate error regions. An obtained wavelet-based edge image is considered as an important weighted coefficient to guide the post-processing. Experimental results demonstrate that the proposed method presents the best performance of the robustness against noise on the Middlebury dataset. Even in the toughest situation with additive Gaussian noise, our method can still achieve the moderate disparity map. In addition, the wider applicability of the proposed method is demonstrated on the KITTI (i.e., Karlsruhe Institute of Technology (KIT) and Toyota Technological Institute at Chicago (TTI-C)) dataset and some typical real-world sequences.