Performance enhancement of Adaptive Support-Weight approach by tuning parameters

Since Yoon and Kweon proposed the Adaptive Support-Weight approach (ASW) based on color similarity and geometric proximity, local stereo matching methods have experienced great improvement, even are better than most of global approaches. In ASW, how to choose proper parameters is a very difficult and ongoing issue. Until now most studies on ASW used empirical parameter values and little work focused on the discussion of the ASW parameter setting. In this paper, we presented a Relational Analysis Method (RAM) to investigate the effects of different parameters on the ASW performance. RAM simultaneously considers the changes of multiple parameters of ASW. Middlebury test platform and four standard test images are applied to conduct experiments. The results show that the different combinations of multiple parameters have significant influences on the ASW performance. The average bad pixels of the four images vary from 45.4% to 8.66%. The ASW performance obtained by using appropriate parameters is much better than several modified ASW methods. Thus through tuning the parameters of ASW, the performance can be greatly improved.