Auto-focusing Evaluation Function for Microscope Using Weighted Neighborhood Correlation

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Auto-focusing Evaluation Function for Microscope Using Weighted Neighborhood Correlation

Article | 更新时间：2020-08-12

- Auto-focusing Evaluation Function for Microscope Using Weighted Neighborhood Correlation
- Optics and Precision Engineering Vol. 16, Issue 1, Pages: 166-171(2008)
- 作者机构：
  
  华中科技大学计算机学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Received：03 July 2007，
  
  Revised：19 August 2007，
  
  Published Online：22 January 2008，
  
  Published：22 January 2008
- 稿件说明：
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王倩,宋恩民,许向阳,刘宏. 基于加权邻域相关性的显微镜自动聚焦函数[J]. 光学精密工程, 2008,16(1):166-171

Auto-focusing Evaluation Function for Microscope Using Weighted Neighborhood Correlation[J]. Optics and precision engineering, 2008, 16(1): 166-171.
王倩,宋恩民,许向阳,刘宏. 基于加权邻域相关性的显微镜自动聚焦函数[J]. 光学精密工程, 2008,16(1):166-171 DOI：

Auto-focusing Evaluation Function for Microscope Using Weighted Neighborhood Correlation[J]. Optics and precision engineering, 2008, 16(1): 166-171. DOI：

摘要

目的：提出了一种基于加权邻域相关性的显微镜自动聚焦函数，并在研究显微镜聚焦原理及成像过程的基础上，分析了显微镜图像中像素邻域灰度相关性及像散现象对聚焦评价的影响。方法：首先，分别计算每幅显微镜序列图像中各像素与其四邻域像素的灰度相关性。然后，计算基于此相关性加权平均值的二次多项式聚焦函数，其中权值根据对应像素与显微镜视场中心的距离来确定。最后，选取该函数值最大的图像为聚焦图像。结果：实验结果表明，与经典的聚焦函数如方差函数、绝对梯度函数、Roberts梯度函数及Tenengrad函数相比，本文方法的聚焦灵敏度因子提高了0.3185~0.3268

噪声环境下聚焦的平均正确率提高了0~40%。结论：该方法能够准确地评价图像聚焦的程度，并具有较高的灵敏度和较强的抗噪性。

Abstract

Objective: A new auto-focusing function for microscope images using the analysis on the four neighborhood gray level correlation has been presented

which is based on the study of the focusing principles and imaging processing of the microscope with the astigmatism considered as an ill factor to the focusing results. Method: firstly

to compute the four neighborhood gray level correlation of each pixel in every image sampled in the focusing processing. Then

to impair the influence of the astigmatism

some remedy measures such as weighted the correlation value by distances between the pixel and image center should be done in computing the focusing evaluation function

which is a quadratic polynomial about the average of the pixel neighborhood correlation. At last

analysis the variations of the function value of the sequential images

the focusing image corresponding the maximum function value. Result: the experiment results indicate that compared with the classical method

our function achieves the focusing sensitivity factor 0.3185~0.3268 higher

and improve the focusing correct rate with 0~40% under noise conditions. Conclusion: Our auto-focusing evaluation function can accurately estimate the focusing extents

and achieve high sensitivity and strong noise immunity.

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