基于深度约束的水下稠密立体匹配

针对双目水下图像匹配不满足空气中常规极线约束的问题,提出一种基于深度约束的半全局算法以实现水下稠密立体匹配.首先采用深度约束确定匹配过程的深度约束搜索区域.然后,基于深度约束区域将绝对差值和梯度计算推广到二维区域并进行加权融合.在深度约束区域内的搜索过程中,采用胜者为王的策略确定某一视差值下的最佳行差及最佳行差下的匹配代价,并将其作为能量函数的数据项应用于半全局算法中,进行匹配代价的聚合.最后采用抛物线拟合法得到亚像素级的稠密视差图.在水下图片上进行的稠密立体匹配结果表明:相较于其他半全局匹配算法,本文算法在极大提高运行速度的前提下,可以获得良好的水下稠密立体匹配效果.

Underwater Dense Stereo Matching Based on Depth Constraint

In order to solve the problem that stereo matching of binocular underwater image could not meet conventional epipolar constraint in the air,a semi-global algorithm for dense stereo matching of underwater image based on depth constraint was proposed.Firstly,a depth constraint was used to determine the searching area during stereo matching;based on depth constraint area,the absolute difference function and gradient calculation were extended to two dimensional directions and then fused by weighted factor.During candidate searching in the depth constraint area,the winner-takes-all was adopted to get the best line aberration under each disparity and their corresponding cost values,these cost values were regarded as data item of semi-global algorithm and an initial disparity map was obtained.Finally the sub pixel dense disparity map was obtained by parabolic fit.Experiments were performed on underwater image to obtain dense disparity map,the results show that compare with other semi-global algorithms,the proposed algorithm could greatly accelerate underwater stereo matching and improve accuracy of matching.