基于神经网络辅助快速迭代滤波法的透明光学元件快速偏转测量中的条纹分离技术

IF 4.2 2区 工程技术 Q2 ENGINEERING, MANUFACTURING
Ting Chen, Pei-De Yang, Xiang-Chao Zhang, Wei Lang, Yu-Nuo Chen, Min Xu
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引用次数: 0

摘要

透明光学元件在光学成像和传感中发挥着重要作用,这些元件的形状质量对光电设备的功能至关重要。因此,迫切需要对先进的透明光学器件进行快速测量。偏转测量法作为一种常用的测量方法,在形状测量方面有着广泛的应用。然而,透明元件的反射偏转测量存在一些难题,如条纹叠加、低反射率和非均匀背景等,严重影响了测量精度。为解决这些问题,本文提出了一种用于透明元件反射偏转测量的单帧条纹分离方法。利用快速迭代滤波方法进行粗边缘分离,并采用卷积神经网络解决信息泄漏和不完全边缘分离问题。神经网络的构建包括改进和完善滤波方法,以实现条纹的精确分离。所提出的方法实现了边缘分离,并形成了上下表面的重建。通过模拟和实验,证明了所提方法的有效性和鲁棒性,测量精度可达到 65 nm 的均方根误差(RMSE)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Separation of fringe patterns in fast deflectometric measurement of transparent optical elements based on neural network-assisted fast iterative filtering method

Separation of fringe patterns in fast deflectometric measurement of transparent optical elements based on neural network-assisted fast iterative filtering method

Transparent optical elements play a significant role in optical imaging and sensing, and the form qualities of these elements are critical to the functionalities of opto-electrical equipment. Therefore, rapid measurement of advanced transparent optical devices is urgently needed. Deflectometry, as a commonly used measurement method, has broad applications in form measurement. However, there are some challenges in the reflective deflectometric measurement of transparent elements, such as fringe superposition, low reflectivity, and non-uniform backgrounds, which severely affect the measurement accuracy. To address these issues, a single-frame fringe separation method is proposed for the deflectometric measurement of transparent elements. A fast iterative filtering method is utilized for coarse fringe separation and a convolutional neural network is adopted to solve the information leakage and incomplete fringe separation. The construction of the neural network involves improving and refining the filtering method to achieve precise separation of fringes. The proposed method achieves fringe separation and forms reconstruction of the upper and lower surfaces. Through simulations and experiments, the effectiveness and robustness of the proposed method are demonstrated, and the measurement accuracy can achieve 65 nm root-of-mean-squared-error (RMSE).

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来源期刊
Advances in Manufacturing
Advances in Manufacturing Materials Science-Polymers and Plastics
CiteScore
9.10
自引率
3.80%
发文量
274
期刊介绍: As an innovative, fundamental and scientific journal, Advances in Manufacturing aims to describe the latest regional and global research results and forefront developments in advanced manufacturing field. As such, it serves as an international platform for academic exchange between experts, scholars and researchers in this field. All articles in Advances in Manufacturing are peer reviewed. Respected scholars from the fields of advanced manufacturing fields will be invited to write some comments. We also encourage and give priority to research papers that have made major breakthroughs or innovations in the fundamental theory. The targeted fields include: manufacturing automation, mechatronics and robotics, precision manufacturing and control, micro-nano-manufacturing, green manufacturing, design in manufacturing, metallic and nonmetallic materials in manufacturing, metallurgical process, etc. The forms of articles include (but not limited to): academic articles, research reports, and general reviews.
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