Dynamic phase measurements employing a pixelated polarizing camera and temporal phase unwrapping algorithms

2021 ◽  
Author(s):  
Yanely Machuca ◽  
Geliztle Parra-Escamilla ◽  
Noel-Ivan Toto-Arellano ◽  
Jorge Flores Nuñez ◽  
David Serrano Garcia
Keyword(s):  
Phase Unwrapping ◽  
Phase Measurements ◽  
Temporal Phase ◽  
Dynamic Phase

scholarly journals An Error-Reduction Method for Temporal Phase Unwrapping Based on Double Three-Step Phase-Shifting and Least-Squares Fitting

2012 ◽  
Vol 6-7 ◽  
pp. 76-81
Author(s):  
Yong Liu ◽  
Ding Fa Huang ◽  
Yong Jiang
Keyword(s):  
Least Squares ◽  
Reduction Method ◽  
Intermediate Phase ◽  
Error Reduction ◽  
Phase Unwrapping ◽  
Phase Shifting ◽  
Surface Measurement ◽  
Acquisition Time ◽  
Least Squares Fitting ◽  
Temporal Phase

Phase-shifting interferometry on structured light projection is widely used in 3-D surface measurement. An investigation shows that least-squares fitting can significantly decrease random error by incorporating data from the intermediate phase values, but it cannot completely eliminate nonlinear error. This paper proposes an error-reduction method based on double three-step phase-shifting algorithm and least-squares fitting, and applies it on the temporal phase unwrapping algorithm using three-frequency heterodyne principle. Theoretical analyses and experiment results show that this method can greatly save data acquisition time and improve the precision.

Modified temporal-phase-unwrapping method for measuring in real time the out-of-plane displacements of the tympanic membrane of Mongolian Gerbil

Optik ◽  
2008 ◽  
Vol 119 (16) ◽  
pp. 783-787 ◽  
Author(s):  
R.A. Martínez-Celorio ◽  
Joris J.J. Dirckx ◽  
Jan A.N. Buytaert ◽  
Luis Martí-López ◽  
Wim Decraemer
Keyword(s):  
Tympanic Membrane ◽  
Real Time ◽  
Mongolian Gerbil ◽  
Phase Unwrapping ◽  
Temporal Phase ◽  
Out Of Plane

3D shape measurement with temporal phase unwrapping and 1-D continuous wavelet transform

2021 ◽  
Author(s):  
Susana L. Burnes-Rudecino ◽  
José de Jesús Villa Hernández ◽  
Gamaliel Moreno ◽  
Ismael de la Rosa ◽  
Efrén González ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Continuous Wavelet Transform ◽  
Phase Unwrapping ◽  
Shape Measurement ◽  
Continuous Wavelet ◽  
3D Shape ◽  
3D Shape Measurement ◽  
Temporal Phase

scholarly journals Temporal phase unwrapping using deep learning

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Wei Yin ◽  
Qian Chen ◽  
Shijie Feng ◽  
Tianyang Tao ◽  
Lei Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Frequency ◽  
Phase Unwrapping ◽  
Motion Artifacts ◽  
Error Sources ◽  
Actual Measurement ◽  
Temporal Phase ◽  
Fringe Patterns ◽  
Frequency Phase

AbstractThe multi-frequency temporal phase unwrapping (MF-TPU) method, as a classical phase unwrapping algorithm for fringe projection techniques, has the ability to eliminate the phase ambiguities even while measuring spatially isolated scenes or the objects with discontinuous surfaces. For the simplest and most efficient case in MF-TPU, two groups of phase-shifting fringe patterns with different frequencies are used: the high-frequency one is applied for 3D reconstruction of the tested object and the unit-frequency one is used to assist phase unwrapping for the wrapped phase with high frequency. The final measurement precision or sensitivity is determined by the number of fringes used within the high-frequency pattern, under the precondition that its absolute phase can be successfully recovered without any fringe order errors. However, due to the non-negligible noises and other error sources in actual measurement, the frequency of the high-frequency fringes is generally restricted to about 16, resulting in limited measurement accuracy. On the other hand, using additional intermediate sets of fringe patterns can unwrap the phase with higher frequency, but at the expense of a prolonged pattern sequence. With recent developments and advancements of machine learning for computer vision and computational imaging, it can be demonstrated in this work that deep learning techniques can automatically realize TPU through supervised learning, as called deep learning-based temporal phase unwrapping (DL-TPU), which can substantially improve the unwrapping reliability compared with MF-TPU even under different types of error sources, e.g., intensity noise, low fringe modulation, projector nonlinearity, and motion artifacts. Furthermore, as far as we know, our method was demonstrated experimentally that the high-frequency phase with 64 periods can be directly and reliably unwrapped from one unit-frequency phase using DL-TPU. These results highlight that challenging issues in optical metrology can be potentially overcome through machine learning, opening new avenues to design powerful and extremely accurate high-speed 3D imaging systems ubiquitous in nowadays science, industry, and multimedia.

Temporal phase unwrapping of digital hologram sequences

2003 ◽  
Vol 42 (29) ◽  
pp. 5846 ◽  
Author(s):  
Giancarlo Pedrini ◽  
Igor Alexeenko ◽  
Wolfgang Osten ◽  
Hans J. Tiziani
Keyword(s):  
Phase Unwrapping ◽  
Digital Hologram ◽  
Temporal Phase

Profile measurement of objects with discontinuous surfaces by modified temporal phase unwrapping

2005 ◽  
Author(s):  
Ailing Tian ◽  
Tomoaki Makihata ◽  
Mitsuo Takeda ◽  
Zhuangde Jiang
Keyword(s):  
Phase Unwrapping ◽  
Profile Measurement ◽  
Temporal Phase
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