亮度
流离失所(心理学)
稳健性(进化)
互相关
粒子图像测速
强度(物理)
光流
光学
粒子(生态学)
物理
计算机科学
人工智能
计算机视觉
数学
图像(数学)
数学分析
机械
地质学
基因
海洋学
生物化学
化学
湍流
心理治疗师
心理学
作者
Hu Li,G Yan,Haidong Zhu,Yan Feng,Jingjing He
摘要
In particle image velocimetry (PIV), the brightness of a particle image sequence may change due to uneven laser intensity distribution and fluctuations in laser output. Consequently, the optical flow method (OFM), which relies on the brightness constancy assumption, becomes unsuitable. The traditional variational OFM is only accurate for small displacement fields but lacks robustness and accuracy when applied to PIV images with intensity variations. In this study, to address these issues, we improve the traditional cross-correlation OFM to establish a high-resolution hybrid cross-correlation optical flow method (CC-OFM) for particle images with large displacement and intensity variations. The data term, which combines the brightness constancy assumption with the gradient constancy assumption, compensates for the intensity changes between the particle image pairs. The proposed CC-OFM is quantitatively evaluated using both synthetic particle images and experimental particle images under various conditions, comparing the displacement results with those obtained using other methods. The results reveal that the proposed CC-OFM provides high accuracy and robustness for particle images with large displacement and intensity variations. Furthermore, its high spatial resolution allows it to capture flow details more effectively than the other methods.
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