Single-shot N-step Phase Measuring Profilometry based on algebraic addition and subtraction

Xu, Chuan; Cao, Yiping; Yang, Na; Wu, Haitao

doi:10.1016/j.ijleo.2023.170665

Cited by 2 publications

(3 citation statements)

References 22 publications

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“…We evaluated the performance of our algorithm on both simulation and real-world data. For comparison, we also implemented the two-stage algorithm [15] and FPTNet [22] as the baselines. The captured fringe patterns and their retrieved phases and reconstruction results were regarded as the ground truth (GT).…”

Section: Resultsmentioning

confidence: 99%

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PSNet: A Deep Learning Model-Based Single-Shot Digital Phase-Shifting Algorithm

Qi,

Liu,

Pang

et al. 2023

Sensors

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In contrast to traditional phase-shifting (PS) algorithms, which rely on capturing multiple fringe patterns with different phase shifts, digital PS algorithms provide a competitive alternative to relative phase retrieval, which achieves improved efficiency since only one pattern is required for multiple PS pattern generation. Recent deep learning-based algorithms further enhance the retrieved phase quality of complex surfaces with discontinuity, achieving state-of-the-art performance. However, since much attention has been paid to understanding image intensity mapping, such as supervision via fringe intensity loss, global temporal dependency between patterns is often ignored, which leaves room for further improvement. In this paper, we propose a deep learning model-based digital PS algorithm, termed PSNet. A loss combining both local and global temporal information among the generated fringe patterns has been constructed, which forces the model to learn inter-frame dependency between adjacent patterns, and hence leads to the improved accuracy of PS pattern generation and the associated phase retrieval. Both simulation and real-world experimental results have demonstrated the efficacy and improvement of the proposed algorithm against the state of the art.

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Section: Resultsmentioning

confidence: 99%

“…Additionally, the number of generated PS steps is limited to four due to the π/2 phase shift, which makes the algorithm more sensitive to noise. Finally, a single-shot N-step PS method [15] has been proposed. With an algebraic addition and subtraction process, arbitrary-step PS patterns can be generated.…”

Section: Introductionmentioning

confidence: 99%

PSNet: A Deep Learning Model-Based Single-Shot Digital Phase-Shifting Algorithm

Qi,

Liu,

Pang

et al. 2023

Sensors

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show abstract

“…N groups, and then obtains the unwrapped phase through phase shift method, phase fusion, and complementary Gray code phase unwrapping method. XU [12] proposed a singleshot N-step phase measuring profilometry, which can directly obtain the phase difference between the target to be measured and the calibration plane, instead of separately calculating the phase with and without the target. These two methods could effectively improve the efficiency of the algorithm, however, it only improves the phase shift method and does not optimize the phase unwrapping method.…”

mentioning

confidence: 99%

A Complementary Gray Code Like Double N-Step Phase Shift Method Without Gray Code Fringe Images

Han,

Yang,

et al. 2023

IEEE Photonics J.

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The complementary Gray code double N-step phase shift method is widely used because of its high detection accuracy and good robustness. However, phase unwrapping methods have the problem of low detection efficiency. In order to solve above problem, this paper proposes a complementary Gray code like double N-step phase shift method without Gray code fringe images. Firstly, the wrapped phases of the different groups of phase shift fringe images are obtained, then we get the phase difference and the fused wrapped phase, and the phase difference is converted into Gray code series. Finally, the unwrapped phase of the measured target is obtained by combining the fused wrapped phase and the Gray code series. This method in this paper improves the detection efficiency. To verify the effectiveness of method proposed in this paper, the method is compared with the complementary Gray code double N-step phase shift method, the complementary Gray code 2N-step phase shift method and the phase derivative variance method. The experimental results show that the method in this paper could effectively obtain the highprecision unwrapped phase, and the efficiency is 33.3% higher than the complementary Gray code double N-step phase shift method and the complementary Gray code 2N-step phase shift method. Index Terms-Double N-step Phase Shift Method; Phase difference; Complementary Gray code Like; fused wrapped phase; Phase unwrapping I. INTRODUCTIONn the three-dimensional field of high-speed measurement, grating projection profilometry is gradually applied to various fields because of its advantages of high precision, non-contact and low cost [1~4]. For example: aerospace field, medical field, cultural relic protection field, etc. Phase unwrapping is an important part of grating projection profilometry. When measured target contour, the arctangent function is used to solve the phase in the phase calculation process. Since the obtained result is the wrapped phase distributed in ( , ]  −[5~8], it is necessary to use the phase unwrapping algorithm to recover the wrapped phase into unwrapped phase. The advantages of complementary Gray code phase unwrapping method are high detection accuracy and robustness, however, it needs to add Gray code images to participate in the detection, which would reduce the detection

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Single-shot N-step Phase Measuring Profilometry based on algebraic addition and subtraction

Cited by 2 publications

References 22 publications

PSNet: A Deep Learning Model-Based Single-Shot Digital Phase-Shifting Algorithm

PSNet: A Deep Learning Model-Based Single-Shot Digital Phase-Shifting Algorithm

A Complementary Gray Code Like Double N-Step Phase Shift Method Without Gray Code Fringe Images

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