摘要
提出了基于小波变换的图像清晰度评价函数。采用大NA(数值孔径)和小NA的显微图像序列,比较分析了本文提出的评价函数和经典的归一化方差函数、熵函数、能量拉普拉斯函数以及另外两种基于小波变换评价函数的清晰度评价性能。同时采用带有标准偏差为25的高斯噪声显微图像序列,比较了这五种评价函数的抗噪能力。实验结果表明:提出的评价函数具有最高的聚焦精度和聚焦分辨率,且具有与抗噪能力最强的归一化方差函数相当的抗噪能力。提出了基于区域选择的自动聚焦方法,实现了处于不同深度的微操作对象的3-D自动聚焦。该评价函数和区域选择聚焦技术可以用于高精度的自动微操作作业中。进一步说明自动调焦是实现自动化微操作的关键技术,而其核心是清晰度评价函数的选取或构建。
A new wavelet-based focus measure was proposed. High NA （Numerical Aperture） and low NA microscope image sequences were used to compare the performance of this focus measure with the classic and popular focus measures Normalized Variance, Entropy, Energy Laplace and other two Wavelet-based High Frequency focus measures. The robustness of these focus measures was also compared using image sequence corrupted by Gaussian white noise with that of standard deviations 25. Experimental results show that the proposed focus measure can provide significantly better depth resolution and accuracy than the comparing ones, and exhibit comparatively good robustness with Normalized Variance. A selective focusing technique for autofocusing on the three dimension microscale objects in different depth was developed. The results also show that this focus measure and selective autofocusing techniques can be widely applied to automated micromanipulation; and that autofocusing is of fundamental importance to automated micromanipulation, and focus measures are the key techniques.
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2006年第6期1063-1069,共7页
Optics and Precision Engineering
基金
黑龙江省教育厅科技项目(No.10051068)
