摘要翻译：
在许多光学测量技术中，条纹图分析是从记录的条纹图中恢复潜在相位分布的核心算法。尽管几十年来进行了广泛的研究，如何从最少的条纹图中以尽可能高的精度提取所需的相位信息仍然是最具挑战性的开放问题之一。受近年来深度学习技术在计算机视觉和其他应用中的成功启发，我们在这里首次证明了深度神经网络可以被训练来执行条纹分析，这大大提高了从单个条纹模式中解调相位的精度。在条纹投影轮廓术的场景下，利用载流子条纹图对该方法的有效性进行了实验验证。实验结果表明，与两种典型的单帧傅里叶变换轮廓术和加窗傅里叶轮廓术相比，该方法在高精度和边缘保持方面具有更好的性能。
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英文标题：
《Fringe pattern analysis using deep learning》
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作者：
Shijie Feng, Qian Chen, Guohua Gu, Tianyang Tao, Liang Zhang, Yan Hu,
  Wei Yin, Chao Zuo
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  In many optical metrology techniques, fringe pattern analysis is the central algorithm for recovering the underlying phase distribution from the recorded fringe patterns. Despite extensive research efforts for decades, how to extract the desired phase information, with the highest possible accuracy, from the minimum number of fringe patterns remains one of the most challenging open problems. Inspired by recent successes of deep learning techniques for computer vision and other applications, here, we demonstrate for the first time, to our knowledge, that the deep neural networks can be trained to perform fringe analysis, which substantially enhances the accuracy of phase demodulation from a single fringe pattern. The effectiveness of the proposed method is experimentally verified using carrier fringe patterns under the scenario of fringe projection profilometry. Experimental results demonstrate its superior performance in terms of high accuracy and edge-preserving over two representative single-frame techniques: Fourier transform profilometry and Windowed Fourier profilometry.
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PDF链接：
https://arxiv.org/pdf/1807.02757