针对光栅图像的快速盲去噪方法 -

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针对光栅图像的快速盲去噪方法

doi:10.37188/CO.2020-0166

张申华^{1, 2,},
杨延西^{1, 3,},
秦峤孟¹

1.
西安理工大学自动化学院，陕西西安 710048
2.
安康学院电子与信息工程学院，陕西安康 725000
3.
陕西省复杂系统控制与智能信息处理重点实验室，陕西西安 710048

基金项目:国家重点研发计划专项（No. 2018YFB1703000）；陕西省重点研发计划项目（No. 2020ZDLGR07-06）；陕西省现代装备绿色制造协同创新中心项目（No. 304-210891702）

详细信息

作者简介:
张申华（1982—），河南信阳人，2010年于西北大学获得硕士学位，现为西安理工大学博士研究生，主要研究方向为光学测量。E-mail：zhang_shenhua@126.com
杨延西（1975—），山东郓城人，博士，教授，2003年于西安理工大学获得博士学位，现为西安理工大学教授、博士生导师，主要研究方向为复杂系统控制、机器视觉和智能机器人。E-mail：yangyanxi@xaut.edu.cn

中图分类号:O439
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- 被引次数:0
出版历程
- 收稿日期:2020-09-11
- 修回日期:2020-10-21
- 网络出版日期:2021-04-09
- 刊出日期:2021-05-14

A fast blind denoising method for grating image

1.
College of Automation, Xi’an University of Technology, Xi’an 710048, China
2.
College of Electronic and Information Engineering, Ankang University, Ankang 725000, China
3.
Shaanxi Key Laboratory of Complex system Control and Intelligent Information Processing, Xi’an 710048, China

Funds:Supported by National Key R&D Program of China (No. 2018YFB1703000); Key R&D Program of Shaanxi Province (No. 2020ZDLGR07-06); Collaborative Innovation Center of Shaanxi Province for Green Manufacturing of Modern Equipment (No. 304-210891702)

摘要

摘要:基于正弦光栅条纹投影的三维测量技术是当前研究的热点问题。然而，受噪声的影响，采集到的光栅条纹图像质量降低，导致提取的相位发生扰动，而相位的提取结果直接决定着测量结果的准确性。实际测量中噪声未知，针对这一问题，本文提出了一种盲去噪方法。首先，根据残差模型，完成光栅条纹图像的真值图像与噪声图像的分离，然后，引入主成分分析技术估计出噪声图像的方差值。最后，根据噪声方差的估计值，利用基于相图的高斯滤波方法，将针对多帧光栅图像的噪声滤波转换到提取的相位图上完成。由实验结果可知，和对比方法相比，本文方法的均方根相位误差最高下降了88.5%，所提方法处理后的相位更加接近测量体的真值相位。本文方法可在最短的执行时间内实现对噪声导致的相位扰动进行抑制。所提方法能够快速处理光栅图像噪声引起的相位误差，在光栅投影测量中具有较强的实用性。
- 光栅图像/
- 盲去噪/
- 主成分分析/
- 相位图滤波
Abstract:The three-dimensional measurement technology based on the projection of sine grating fringe image is a hot-topic. However, due to the influence of noise, the quality of the captured grating image is worse, resulting in the disturbance of the extracted phase, which directly determines the accuracy of the measurement. Since the noise is unknown in actual measurement, a blind denoising method is proposed in this paper. Firstly, according to the residual model, the grating fringe image is separated into the true value and the noise, then the Principal Component Analysis (PCA) technology is introduced to estimate the variance of the noise. Finally, according to the estimated value of the variance, the filtering on multi-frame fringe images is replaced by employing Gaussian filtering on the phase map. In contrast to other methods, the results of the proposed method showed that the Root Mean Square Error (RMSE) decreased by 88.5% (up to most), which indicated that the phase values of the proposed method were closer to the ground-truth of the measured object. By employing the proposed method, the phase disturbance caused by noise were significantly suppressed in the shortest execution time. The proposed method can quickly deal with the phase error caused by the noise of the grating image and has strong practicability in the grating image projection measurement.
- grating image/
- blind denoising/
- PCA/
- phase map filtering

HTML全文

图 1PCA方法对仿真噪声的估计

Figure 1.The estimation of simulation noise by PCA method

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图 2主成分分量占比

Figure 2.Proportion of principal component

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图 3测量系统结构图

Figure 3.Framework of measuring system

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图 4采集的光栅图像

Figure 4.Captured grating image

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图 5几种不同方法的滤波效果

Figure 5.Filtering effect obtained by different methods

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图 6各种方法的相位差

Figure 6.Phase errors obtained by different methods

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表 1仪器设备型号和参数

Table 1.The instrument types and parameters

仪器设备	型号	主要性能及参数
投影仪	DLP4500	分辨率912 pixel×1140 pixel
工业相机	MV-UB130M	分辨率1024 pixel×1280 pixel 曝光时间：200 ms 帧率：30 frame/s 信噪比：45 dB
电脑	Intel Core (TM) i5-8250U CPU	主频：1.6 GHz

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表 2各种方法的噪声方差估计值

Table 2.The estimated noise variances of different methods

方法	文献[5]方法	文献[7]方法	文献[8]方法	本文方法
估计值	−	3.3737	1.6025	2.7823

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表 3几种算法的量化指标对比

Table 3.Comparison of quantitative indicators for different methods

	中值滤波方法	Ref.[10] 方法	Ref.[12] 方法	Ref.[13] 方法	本文方法
MPE	0.3856	9.2343	1.8268	1.7301	0.7003
RMSE	0.0961	0.5122	0.1029	0.1038	0.0591

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表 4几种算法的执行时间对比

Table 4.Comparison of computing time for different methods (s)

对比方法	中值滤波方法	Ref.[10] 方法	Ref.[12] 方法	Ref.[13] 方法	本文方法
执行时间	16.12	758.36	7.18	11.67	6.74

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