基于自适应权重的立体匹配优化算法

doi:10.19678/j.issn.1000-3428.0056991

摘要/Abstract

摘要： 为解决现有立体匹配算法对低纹理以及视差不连续区域匹配效果较差的问题，提出一种改进的立体匹配优化算法。在传统自适应权重算法匹配代价的基础上，融合高斯差分图像差分信息，即左右图像高斯差分图的差分，重新定义其初始匹配代价，增加算法在视差不连续区域的鲁棒性，并加入边缘约束和视差边缘约束迭代聚类以及基于高斯差分图的自适应窗口算法，保证改进算法在低纹理区域的匹配性能，消除坏点与视差空洞。将该算法与传统自适应权重匹配算法分别在Middlebury数据集上进行匹配实验，结果表明，该算法平均性能提升了15.05%，明显优于传统自适应权重匹配算法。

关键词: 立体匹配, 自适应权重, 低纹理, 视差不连续, 边缘约束, 自适应窗口

Abstract: In order to solve the problem that the existing stereo matching algorithm has poor performance for low texture and disparity-discontinuous regions, this paper proposes an improved stereo matching optimization algorithm. Based on the matching cost of the traditional adaptive weighting algorithm, the difference information of the Gaussian difference image (referring to the difference between the left and right image Gaussian difference map) is fused, and the initial matching cost is redefined to enhance the robustness of the algorithm for disparity-discontinuous regions. Then it adds edge constraint, parallax edge constraint iterative clustering and an adaptive window algorithm based on Gaussian difference map to ensure the matching performance of the algorithm in low-texture regions and eliminate dead pixels and parallax holes. The algorithm and the traditional adaptive weight matching algorithm are tested on the Middlebury dataset. The results show that the average performance of the proposed algorithm is improved by 15.05%, which is significantly better than that of the traditional adaptive weight matching algorithm.

Key words: stereo matching, adaptive weight, low texture, disparity discontinuity, edge constraint, adaptive window

中图分类号:

TP391

文斌, 朱晗. 基于自适应权重的立体匹配优化算法[J]. 计算机工程, 2021, 47(4): 268-276.

WEN Bin, ZHU Han. Stereo Matching Optimization Algorithm Based on Adaptive Weights[J]. Computer Engineering, 2021, 47(4): 268-276.

https://www.ecice06.com/CN/Y2021/V47/I4/268

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