| 摘要: |
| 由粗略到精细，分层策略和跨尺度的代价聚合在一定程度上有效地扩展了代价聚集并且能够生成高精度的视差图.这类方法致力于在弱纹理区域找到正确的匹配点从而提高匹配率.然而，这类方法必须以多尺度为前提，通常需要借助图像金字塔.另外，误差的传播以及薄壁结构的复原不理想限制了它们的应用.针对弱纹理匹配的问题，提出了一种通用的融合灰色尺度的代价聚合的立体匹配框架.鉴于高斯滤波后的灰度图像能够更好地表示匹配图像对中的弱纹理区域，该代价聚合融合了灰度图像的代价聚合.同时，算法不需要降采样以及建立图像金字塔，这加快了聚合速度.此外，还引入了引导图像滤波和快速加权中值滤波，用于代价聚合和视差求精.同时，在进行视差选择时，为了避免WTA（winner-take-all）带来的歧义，利用代价聚合后最小值和次小值之间的相互关系来确定最后的视差值.最终，在Middlebury测试平台上的实验结果表明：融合灰色尺度的代价聚合的立体匹配能够有效地提高视差的精度. |
| 关键词: 引导图像滤波 局部立体匹配 快速加权中值滤波 融合灰色尺度 |
| DOI： |
| 分类号: |
| 基金项目:国家自然科学基金（61572333） |
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| Fusion of Gray Scale Cost Aggregation for Stereo Matching |
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HAN Xian-Jun, YANG Hong-Yu
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College of Computer Science, Sichuan University, Chengdu 610065, China
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| Abstract: |
| Coarse-to-Fine (CTF), hierarchical strategy, and cross-scale cost aggregation have efficiently expanded cost aggregation methods and yield a highly accurate disparity map to some extent. They are committed to providing a good trick to find the correct matching points in the weak texture region. However, these methods must be multi-scale as the prerequisite and usually need the assistance of image pyramid. They are limited to the propagation of errors from coarse to fine levels and poor recovery of thin structures. In this study, a generic fusion of gray scale cost aggregation framework is proposed which encourages the initial cost aggregation to integrate the cost aggregation of gray image. The main purpose of the gray image after Gaussian filter is to match the corresponding pixels in the weak texture region of the image better. Meanwhile, it does not need to scale down to build the image pyramid and aggregate cost at each scale and thus accelerate the step of cost aggregation. Furthermore, guided image filtering and fast weighted median filtering are introduced in this study for cost aggregation and disparity refine. In addition, to avoid choosing ambiguity that WTA (winner-take-all) brings, the interrelationship between the minimum value of cost aggregation and the second smallest value is utilized to determine the final disparity. It is shown that the fusion of gray scale cost aggregation framework is important as it effectively leads to significant improvement evaluated on Middlebury. |
| Key words: guided image filtering local stereo matching fast weighted median filtering fusion of gray scale |
