应用属性距离加权平均滤波提高CCD光斑的亚像素定位精度

熊刚; 丁天怀; 王鹏

doi:10.3788/OPE.20122005.1102

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应用属性距离加权平均滤波提高CCD光斑的亚像素定位精度

信息科学 | 更新时间：2020-08-12

- 应用属性距离加权平均滤波提高CCD光斑的亚像素定位精度
- Improvement of sub-pixel location accuracy of light-spot on CCD with ADWA filter
- 光学精密工程 2012年20卷第5期页码：1102-1109
- 作者机构：
  
  清华大学精密仪器与机械学系北京,100084
- 作者简介：
- 基金信息：
  
  清华大学校企合作资助项目(No.041514028)()
- DOI：10.3788/OPE.20122005.1102
  中图分类号： TN386.5
- 收稿日期：2012-01-28，
  
  修回日期：2012-03-01，
  
  网络出版日期：2012-05-10，
  
  纸质出版日期：2012-05-10
- 稿件说明：
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熊刚, 丁天怀, 王鹏. 应用属性距离加权平均滤波提高CCD光斑的亚像素定位精度[J]. 光学精密工程, 2012,20(5): 1102-1109

XIONG Gang, DING Tian-huai, WANG Peng. Improvement of sub-pixel location accuracy of light-spot on CCD with ADWA filter[J]. Editorial Office of Optics and Precision Engineering, 2012,20(5): 1102-1109
熊刚, 丁天怀, 王鹏. 应用属性距离加权平均滤波提高CCD光斑的亚像素定位精度[J]. 光学精密工程, 2012,20(5): 1102-1109 DOI： 10.3788/OPE.20122005.1102.

XIONG Gang, DING Tian-huai, WANG Peng. Improvement of sub-pixel location accuracy of light-spot on CCD with ADWA filter[J]. Editorial Office of Optics and Precision Engineering, 2012,20(5): 1102-1109 DOI： 10.3788/OPE.20122005.1102.

摘要

针对现有滤波方法无法克服CCD光斑信号的噪声去除和特征保持之间的矛盾

由图像噪声引起的随机误差成为影响光斑定位精度主要因素的问题

提出了用属性距离加权平均(ADWA)方法对光斑图像进行滤波处理的方案。根据含噪信号的属性分离信号和噪声

通过增加属性的方式来进一步缓解噪声去除与信号特征保持之间的矛盾。实验结果表明

在基于位置、幅值的二元属性距离加权平均(2-ADWA/LV)的基础上

引入"梯度"属性构成3-ADWA/LVG后对光斑信号进行滤波处理

能将光斑定位精度提高20%以上

使定位结果标准差减小到0.024 pixel。实验表明

用ADWA对光斑信号滤波

可通过挖掘和引入新的属性来进一步去除光斑信号噪声同时增强信号特征保持效果

从而为提高光斑的亚像素定位精度提供一条有效途径。

Abstract

As existing filtering methods could not overcome the contradiction between noise removal and feature preserving for the light-spot signals on a CCD

the random error resulted from image noises has become a key constraint on the location accuracy of the light-spot images. Therefore

a filtering method named Attribute Distance Weighted Average (ADWA) is proposed to denoise the light-spot images. The method separates signals and noises based on their attributes

and alleviates the contradiction between noise removal and feature preserving of the signals progressively through introducing new attributes. Experimental results show that as compared with the bivariate ADWA based on "location" and "value"(2-ADWA/LV)

the sub-pixel location accuracy of light-spot has been improved by more than 20% and the standard deviation of location reduced to 0.024 pixel by introducing a 3-ADWA/LVG formed by adding an attribute "gradient" to 2-ADWA/LV. It concludes that when the ADWA is used to denoise the light-spot image

the filtering performance can be further enhanced progressively in both noise removal and feature preserving through introducing new attributes. The ADWA gives a new effective way to further improve the sub-pixel location accuracy of the light-spot.

关键词

Keywords

references

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