Abstract:
Binocular stereo matching can obtain the accuracy and dense disparity map by comparing two images.However,the utilization of dynamic programming algorithms may result in some shortcomings,such as stripe-like and low accuracy.Aiming these problems,this paper proposes a new stereo matching algorithm based on multiple neighbors' nonlinear diffusion.Firstly,absolute difference test method is used to build disparity space image in raw costs computation period.And then,according to the constraint relation between rows and columns,multiple neighbors' nonlinear diffusion of costs aggregation is proposed to improve the global costs function.Finally,dense disparity maps during the global optimization process are obtained by the edges-optimized DP optimization.The experiment results via Middlebury test images show that the proposed algorithm attains the average PBM 5.60% and raises the accuracy 39.9% than ⅡDP.Moreover,the problem of stripe-like is well solved and the edge-blurring is also improved.Compared with other global matching methods,the proposed algorithm reduces PBM by 38.2% and has 9 of 11 indexes to rank the first.