Image registration of polarimeters

Jin-kui CHU; Wei LIN; Ran ZHANG; Yong-tai CHEN

doi:10.3788/OPE.20182605.1181

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Image registration of polarimeters

Information Sciences | 更新时间：2020-08-13

- Image registration of polarimeters
- Optics and Precision Engineering Vol. 26, Issue 5, Pages: 1181-1190(2018)
- 作者机构：
  
  大连理工大学机械工程学院, 辽宁大连 116023
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182605.1181
  CLC： P161.3;TP751.1
- Received：14 August 2017，
  
  Accepted：26 September 2017，
  
  Published：25 May 2018
- 稿件说明：
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Jin-kui CHU, Wei LIN, Ran ZHANG, et al. Image registration of polarimeters[J]. Optics and precision engineering, 2018, 26(5): 1181-1190.
DOI：

Jin-kui CHU, Wei LIN, Ran ZHANG, et al. Image registration of polarimeters[J]. Optics and precision engineering, 2018, 26(5): 1181-1190. DOI： 10.3788/OPE.20182605.1181.

摘要

分振幅式偏振探测成像系统的各分光路图像之间存在位置误差，率先完成各分光路图像之间的图像配准是进行偏振探测的前提条件。针对探测过程中，目标特征不明显、图像特征难以提取、各分光路图像间灰度变化较大的问题，提出适用于分振幅式偏振探测成像系统各分光路图像的相似性度量函数，并在此基础上，完成各分光路图像间的配准工作。首先，根据图像间的位置误差会造成偏振信息图像中出现信息异常区域的原理，研究了相似性度量函数的提取算法；接着，根据探测系统的各分光路的成像特点，确定图像间的几何变换参数；以遗传算法作为参数优化搜索算法，搜索得到最优的几何变换参数，完成整个图像配准算法的设计；最后，分别利用构造图像和实际采集图像，对配准算法进行了验证，并以图像间互信息值（MI）衡量图像配准的精度。实验结果表明：配准后的构造图像的MI为2.692 5，高于特征配准方法的实现精度；实际采集图像配准后的MI达1.849 3，同样高于特征配准方法的实现精度。基本满足偏振探测系统的图像配准需求。

Abstract

There are position errors between the images from each split optical path of the amplitude-division imaging polarimeters

image registration between the images from each split optical path is a precondition for polarized detection. In order to solve the problem that the features of the target are not obvious and difficult to extract

and that the change of gray scale of the image is large

a function of similarity measurement was proposed and it was suitable for the image registration of the amplitude-division imaging polarimeter. Based on the function

the images from each split optical path had been registered. First

according to the principle that the position error between the images can cause the abnormal information areas in the polarized image

the algorithm of extracting the similarity metric function was discussed. Next

according to the characteristics of the imaging system

the parameters of the geometric transformation between images were determined. Then

the genetic algorithm as the parameter optimization search algorithm was used to get optimal parameters of the geometric transformation between images

and the image registration algorithm was finished. Finally

the algorithm was verified by using constructed images and collected images respectively

and the image mutual information value (MI) is determined as the metric of the accuracy of image registration. The experimental results show that the MI between reference image and registered sensed image is 2.692 5 for the constructed image

and it is 1.849 3 for collected images.It indicates that the accuracy of registration by using this method is higher than that by using method based on feature. It can satisfy the registration requirements of the system.

关键词

Keywords

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