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首页> 外文期刊>Optics and Lasers in Engineering >Phase unwrapping method for point diffraction interferometer based on residual auto encoder neural network
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Phase unwrapping method for point diffraction interferometer based on residual auto encoder neural network

机译：基于残余自动编码器神经网络的点衍射干涉仪的相位展开方法

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摘要

In this paper, a phase unwrapping method based on Residual Auto Encoder Network is proposed. Phase unwrapping is regarded as a multiple classification problem and it will be solved by the trained network model. Through training and validation stages, optimal network model can be served as a predictor to predict wrap count distribution map of wrapped phase. Then merge wrapped phase and count together to complete unwrapping. Software simulation and hardware acquisition are the sources for the generation of training dataset. To further improve the accuracy of unwrapping, an image analysis based optimization method is designed that can remove misclassification and noise points in initial result. In addition, phase data stitching by Iterative Closest Point (ICP) is adopted to realize dynamic resolution and enhance the flexibility of method. Point diffraction interferometer (PDI) and multi-step phase extraction technique are introduced, which is the foundation of proposed method. It can be concluded from the experiments that the proposed method is superior to state-of-art ones in terms of unwrapping performance, time efficiency, anti-noise ability and flexibility.
机译：本文提出了一种基于残差自动编码器网络的相位展开方法。相位展开被视为多分类问题，它将通过培训的网络模型解决。通过训练和验证阶段，最佳网络模型可以用作预测包裹阶段的包裹数分布图的预测器。然后合并包阶段并将数量一起完成展开。软件仿真和硬件采集是生成训练数据集的来源。为了进一步提高展开的准确性，设计了一种基于图像分析的优化方法，可以在初始结果中清除错误分类和噪声点。另外，采用迭代最接近点（ICP）拼接的相位数据来实现动态分辨率并增强方法的灵活性。引入点衍射干涉仪（PDI）和多步相萃取技术，这是所提出的方法的基础。从实验中可以得出结论，所提出的方法在展开性能，时间效率，抗噪声能力和灵活性方面优于最先进的方法。

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  • 来源
    《Optics and Lasers in Engineering》 |2021年第3期|106405.1-106405.10|共10页
  • 作者

    Zhao Zhuo; Li Bing; Kang Xiaoqin; Lu Jiasheng; Liu Tongkun;

  • 作者单位

    Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn 99 Yanxiang Rd Xian 710054 Shaanxi Peoples R China|Xi An Jiao Tong Univ Int Joint Res Lab Micro Nano Mfg & Measurement Te Xian 710049 Shaanxi Peoples R China;

    Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn 99 Yanxiang Rd Xian 710054 Shaanxi Peoples R China|Xi An Jiao Tong Univ Int Joint Res Lab Micro Nano Mfg & Measurement Te Xian 710049 Shaanxi Peoples R China;

    Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn 99 Yanxiang Rd Xian 710054 Shaanxi Peoples R China;

    Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn 99 Yanxiang Rd Xian 710054 Shaanxi Peoples R China;

    Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn 99 Yanxiang Rd Xian 710054 Shaanxi Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Phase unwrapping; Optical metrology; Deep learning; Interferometry; Deep neural network; Iterative closest point;

    机译：阶段展开;光学计量;深度学习;干涉测量;深神经网络;迭代最近的点;

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