Pixel-by-pixel absolute phase retrieval using three phase-shifted fringe patterns without markers

Jiang, Chufan; Li, Beiwen; Zhang, Song

doi:10.1016/j.optlaseng.2016.12.002

Cited by 50 publications

(10 citation statements)

References 27 publications

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“…Multi-frequency temporal phase unwrapping techniques have been optimized by utilizing the inherent information redundancy in the average intensity and the intensity modulation of the fringe images, allowing for absolute phase retrieval with the reduced number of patterns 32 , 295 . Geometric constraints were introduced in FPP to solve the phase ambiguity problem without additional image acquisition 175 , 183 . Despite these extensive research efforts for decades, how to extract the absolute (unambiguous) phase information, with the highest possible accuracy, from the minimum number (preferably single shot) of fringe patterns remains one of the most challenging open problems in optical metrology.…”

Section: Invoking Deep Learning In Optical Metrology: Principles and ...mentioning

confidence: 99%

Deep learning in optical metrology: a review

et al. 2022

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With the advances in scientific foundations and technological implementations, optical metrology has become versatile problem-solving backbones in manufacturing, fundamental research, and engineering applications, such as quality control, nondestructive testing, experimental mechanics, and biomedicine. In recent years, deep learning, a subfield of machine learning, is emerging as a powerful tool to address problems by learning from data, largely driven by the availability of massive datasets, enhanced computational power, fast data storage, and novel training algorithms for the deep neural network. It is currently promoting increased interests and gaining extensive attention for its utilization in the field of optical metrology. Unlike the traditional “physics-based” approach, deep-learning-enabled optical metrology is a kind of “data-driven” approach, which has already provided numerous alternative solutions to many challenging problems in this field with better performances. In this review, we present an overview of the current status and the latest progress of deep-learning technologies in the field of optical metrology. We first briefly introduce both traditional image-processing algorithms in optical metrology and the basic concepts of deep learning, followed by a comprehensive review of its applications in various optical metrology tasks, such as fringe denoising, phase retrieval, phase unwrapping, subset correlation, and error compensation. The open challenges faced by the current deep-learning approach in optical metrology are then discussed. Finally, the directions for future research are outlined.

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Section: Invoking Deep Learning In Optical Metrology: Principles and ...mentioning

confidence: 99%

Deep learning in optical metrology: a review

et al. 2022

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“…[27], Jiang et al . [28] and Dai et al . [29] take phase map of objects or planes with known depth as reference.…”

Section: Related Workmentioning

confidence: 99%

Absolute phase unwrapping with SVM for fringe‐projection profilometry

Xiang

et al. 2020

IET image process

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“…By calculating a least square solution using the triple-view information, more accurate 3D surface data can be reconstructed. Compared with the related works [17][18][19][20][21][22][23][24][25][26], the proposed method makes adequate use of triple-view information to achieve more accurate 3D measurement with relative less computational correlation cost, which has certain instructive significance in the field of 3D profilometry and practical measurement engineering. Three-step phase-shifting algorithm is suitable for highspeed measurements due to its advantage of requiring the minimum number of patterns for phase extraction.…”

Section: Introductionmentioning

confidence: 99%

Accurate and dynamic 3D shape measurement with digital image correlation-assisted phase shifting

Hu¹,

Yang²,

Zheng³

et al. 2021

Meas. Sci. Technol.

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Phase shifting profilometry (PSP) has been widely used in structured-light (SL) system for three-dimensional (3D) shape measurements, but the speed of PSP technique is limited by the increased phase-shifting patterns. This paper proposes an accurate and dynamic 3D shape measurement method by projecting only four patterns including three-step phase-shifting patterns and one speckle pattern. Three-step phase-shifting images are used to obtain the initial unwrapped phase map with phase ambiguity. Based on the principle of digital image correlation (DIC) and multi-view geometry, the absolute phase can be recovered reliably without requiring any embedded features or pre-defined information of the object. To improve the measurement accuracy, the projector coordinate is used as the measuring coordinate to establish a novel stereo structured-light system model. By solving a least square solution using the triple-view information, accurate 3D surface data can be reconstructed. The experimental results indicate that the proposed method can perform high-speed and accurate 3D shape measurements with an accuracy of 10.64 μm, which is superior to conventional methods and has certain instructive significance for 3D profilometry and measurement engineering.

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Pixel-by-pixel absolute phase retrieval using three phase-shifted fringe patterns without markers

Cited by 50 publications

References 27 publications

Deep learning in optical metrology: a review

Deep learning in optical metrology: a review

Absolute phase unwrapping with SVM for fringe‐projection profilometry

Accurate and dynamic 3D shape measurement with digital image correlation-assisted phase shifting

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