| 叠焦三维显微视觉测量聚焦评价算法研究 |
| Research on focus measure algorithm of focus stacking 3D microscopic vision measurement |
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| 中文摘要: |
| 为了精确、快速提取叠焦三维显微视觉测量的叠焦图像序列像素点聚焦位置，提出了基于最大梯度的聚焦评价算法。设计了2个带通掩膜算子和4个高通掩膜算子，在带通信号和高通信号中提取每个像素点对于相邻像素点强度变化的最大值来表示该点的聚焦程度，以提高聚焦评价函数提取梯度信息的能力。采用自适应梯度阈值分割算法提高了聚焦评价的灵敏度，降低了聚焦评价曲线的波动及次峰的影响。开展实验对算法的实际性能进行验证，结果表明：相较经典的空间域聚焦评价算法，基于最大梯度的聚焦评价算法的鲁棒性、灵敏度、无偏性和单峰性均显著提高，且具有很好的实用性，能够有效满足复杂轮廓微纳米级三维显微测量聚焦评价要求。 |
| 英文摘要: |
| In order to accurately and quickly extract the focus position of pixels in the image sequence of the focus stacking 3D microscopic vision measurement, a focus measure algorithm based on the maximum gradient is proposed. Two band-pass mask operators and four high-pass mask operators are designed, and the maximum value of the intensity change of each pixel with respect to adjacent pixels is extracted from the band-pass signal and the high-pass signal to indicate the degree of focus, so as to improve the ability of focus measure function to extract gradient information. The adaptive gradient threshold segmentation algorithm improves the sensitivity of focus measure and reduces the influence of large fluctuations and sub-peaks of the focus measure curve. The experimental results show that, compared with the classical spatial focus measure algorithm, the focusing measure algorithm based on maximum gradient has significantly improved its robustness, sensitivity, unbiasedness and unimodality, and has good practicability, which can effectively meet the requirements of focusing measure of micro-nanometer scale 3D microscopic measurement of complex contours. |
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| 中文关键词: 叠焦 显微成像 聚焦评价 最大梯度 视觉测量 |
| 英文关键词:focus stacking microscopic imaging focus measure maximum gradient vision measurement |
| 基金项目: |
| DOI：10.11823/j.issn.1674-5795.2023.01.09 |
| 引用本文：吴昂, 卢荣胜.叠焦三维显微视觉测量聚焦评价算法研究[J].计测技术,2023,(1):. |
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