Polarized image registration method based on phase correlation and sub-graph

Yi-liang WAN; Jian-li WANG; Nan ZHANG; Kai-nan YAO; Hao-jing WANG

doi:10.3788/YJYXS20193405.0530

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Polarized image registration method based on phase correlation and sub-graph

Image Processing | 更新时间：2020-07-05

- Polarized image registration method based on phase correlation and sub-graph
- Chinese Journal of Liquid Crystals and Displays Vol. 34, Issue 5, Pages: 530-536(2019)
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所, 吉林长春 130033
  2.中国科学院大学, 北京 100049
  3.中国科学院国家天文台长春人造卫星观测站, 吉林长春 130117
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20193405.0530
  CLC：
- Received：22 January 2019，
  
  Accepted：14 March 2019，
  
  Published：05 May 2019
- 稿件说明：
移动端阅览
Yi-liang WAN, Jian-li WANG, Nan ZHANG, et al. Polarized image registration method based on phase correlation and sub-graph[J]. Chinese journal of liquid crystals and displays, 2019, 34(5): 530-536.
DOI：

Yi-liang WAN, Jian-li WANG, Nan ZHANG, et al. Polarized image registration method based on phase correlation and sub-graph[J]. Chinese journal of liquid crystals and displays, 2019, 34(5): 530-536. DOI： 10.3788/YJYXS20193405.0530.

摘要

为了解决多目偏振相机在获取图像中需要进行准确配准的问题，本文提出了一种基于子图像的相位相关算法。算法首先对图像进行噪声减弱与细节增强，并进行粗配准。之后在将图像分割为多个子图，对每个对应区域的子图进行配准，并剔除配准误差较大的子图，以减少它们对于最终配准结果的不良影响。最后根据这些子图的配准参数来计算单应性矩阵，完成最终的配准。实验结果表明，对于3种不同场景的图像，相比于传统方法，本文方法的配准结果归一化互信息值均有所提高，至多提高0.067%且图像细节更清晰。与传统算法相比，本文算法对于低清晰度、大噪声的图像具有更好的配准效果。

Abstract

To solve the problem that multi-lens polarization cameras need accurate registration in acquiring images

this paper proposes a phase correlation algorithm based on sub-image. The algorithm first performs noise reduction and detail enhancement on the image followed by a coarse registration. Then the image is divided into multiple subgraphs

the subgraph of each corresponding area is registered

and the subgraphs with large registration errors is eliminated to reduce their adverse effects on the final result. Finally the univariate matrix is calculated according to the registration parameters of the remaining subgraphs to complete the final registration. The experimental results show that for all three different types of images

the proposed algorithm improves both the normalized mutual information values

up to 0.067%

and the image details. Compared with the original algorithm

the proposed algorithm has a better registration effect for low definition and high noise images.

关键词

Keywords

references

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