Stereo matching algorithm based on Census transformation and guided filter

Stereo matching is one of the key steps in binocular stereo vision system, and its accuracy has a significant impact on the subsequent research. The Census algorithm is widely used because of its advantages of simplicity and clarity, good running effect and strong real-time performance. However, the Census stereo matching algorithm also has some disadvantages, such as the center point of transform window is easily disturbed by the external conditions and the accuracy of the depth discontinuous region is low. Therefore, a new stereo matching algorithm based on Census transformation and guided filter was proposed. In the stage of Census transformation, the impact of the external interference was reduced by calculating the pixel average around the transform window. And at the same time, in the stage of cost aggregation, the guided filter with tipping characteristics and its computational complexity was independent of the size for filter nuclear was introduced as the adaptive weight. The experimental results show that the average mistake matching error of the proposed algorithm is 6.03% on the Middlebury test platform. Compared with the current average mistake matching rate of 16.2% for Census stereo matching algorithm, the matching effect is obviously improved, and the algorithm has high efficiency and good radiation invariance.