Semi-global Stereo Matching Algorithm Based on Census Transform

Abstract:
Aiming at the problem that the center point of traditional Census transform window is vulnerable to the noise of outer boundary environment and the area of partial depth discontinuous area has low matching precision in stereo matching, a semi-global stereo matching algorithm based on anti-noise Census transform is proposed. Parallax graphs with a higher degree of match can be output. Firstly, by preprocessing the image, more information of the image can be retained and noise interference can be removed. Meanwhile, the Census algorithm is improved to overcome the dependence on the center pixel of the window and enhance the anti-interference of the initial generation value against noise. Secondly, in the cost aggregation stage, Sobel edge detection is used for cost aggregation. Then, the initial aberration is determined according to the rule that the winner is king. The last is the parallax optimization, through the left-right consistency detection strategy to reduce the false match points, improve the parallax accuracy of occlusion points, and finally get a fine parallax map. Compared with other stereo matching algorithms on Middlebury Vision website, the experimental results show that the proposed algorithm is superior to other stereo matching algorithms.