A wavelet-based focus measure and 3-D autofocusing for microscope images

WANG Yi-wen; LIU Xian-li; XIE Hui

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A wavelet-based focus measure and 3-D autofocusing for microscope images

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- A wavelet-based focus measure and 3-D autofocusing for microscope images
- Optics and Precision Engineering Vol. 14, Issue 6, Pages: 1063-1069(2006)
- 作者机构：
  
  1. 哈尔滨理工大学现代制造技术与刀具开发省高校重点实验室,黑龙江哈尔滨,150080
  2. 哈尔滨工业大学机器人研究所,黑龙江哈尔滨,150001
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP113
- Received：12 April 2006，
  
  Revised：27 October 2006，
  
  Published Online：30 December 2006，
  
  Published：30 December 2006
- 稿件说明：
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WANG Yi-wen, LIU Xian-li, XIE Hui. A wavelet-based focus measure and 3-D autofocusing for microscope images[J]. Optics and precision engineering, 2006, 14(6): 1063-1069.
DOI：

WANG Yi-wen, LIU Xian-li, XIE Hui. A wavelet-based focus measure and 3-D autofocusing for microscope images[J]. Optics and precision engineering, 2006, 14(6): 1063-1069. DOI：

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。

Abstract

A new wavelet-based focus measure was proposed. High

(Numerical Aperture) and low

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.

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