Temporal phase unwrapping using deep learning

The multi-frequency temporal phase unwrapping (MF-TPU) method, as a classical phase unwrapping algorithm for fringe projection profilometry (FPP), is capable of eliminating the phase ambiguities even in the presence of surface discontinuities or spatially isolated objects. For the simplest and most efficient case, two sets of 3-step phase-shifting fringe patterns are used: the high-frequency one is for 3D measurement and the unit-frequency one is for unwrapping the phase obtained from the high-frequency pattern set. The final measurement precision or sensitivity is determined by the number of fringes used within the high-frequency pattern, under the precondition that the phase can be successfully unwrapped without triggering the fringe order error. Consequently, in order to guarantee a reasonable unwrapping success rate, the fringe number (or period number) of the high-frequency fringe patterns 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. Inspired by recent successes of deep learning techniques for computer vision and computational imaging, in this work, we report that the deep neural networks can learn to perform TPU after appropriate training, as called deep-learning based temporal phase unwrapping (DL-TPU), which can substantially improve the unwrapping reliability compared with MF-TPU even in the presence of different types of error sources, e.g., intensity noise, low fringe modulation, and projector nonlinearity. We further experimentally demonstrate for the first time, to our knowledge, that the high-frequency phase obtained from 64-period 3-step phase-shifting fringe patterns can be directly and reliably unwrapped from one unit-frequency phase using DL-TPU

Demodulation of Spatial Carrier Images: Performance Analysis of Several Algorithms Using a Single Image

http://link.springer.com/article/10.1007%2Fs11340-013-9741-6#Optical full-field techniques have a great importance in modern experimental mechanics. Even if they are reasonably spread among the university laboratories, their diffusion in industrial companies remains very narrow for several reasons, especially a lack of metrological performance assessment. A full-field measurement can be characterized by its resolution, bias, measuring range, and by a specific quantity, the spatial resolution. The present paper proposes an original procedure to estimate in one single step the resolution, bias and spatial resolution for a given operator (decoding algorithms such as image correlation, low-pass filters, derivation tools ...). This procedure is based on the construction of a particular multi-frequential field, and a Bode diagram representation of the results. This analysis is applied to various phase demodulating algorithms suited to estimate in-plane displacements.GDR CNRS 2519 “Mesures de Champs et Identification en Mécanique des Solide

A 3D Sensor Based on a Profilometrical Approach

An improved method which considers the use of Fourier and wavelet transform based analysis to infer and extract 3D information from an object by fringe projection on it is presented. This method requires a single image which contains a sinusoidal white light fringe pattern projected on it, and this pattern has a known spatial frequency and its information is used to avoid any discontinuities in the fringes with high frequency. Several computer simulations and experiments have been carried out to verify the analysis. The comparison between numerical simulations and experiments has proved the validity of this proposed method

Snapshot Three-Dimensional Surface Imaging With Multispectral Fringe Projection Profilometry

Fringe Projection Profilometry (FPP) is a popular method for non-contact optical surface measurements, including motion tracking. The technique derives 3D surface maps from phase maps estimated from the distortions of fringe patterns projected onto the surface of an object. Estimation of phase maps is commonly performed with spatial phase retrieval algorithms that use a series of complex data processing stages. Researchers must have advanced data analysis skills to process FPP data due to a lack of availability of simple research-oriented software tools. Chapter 2 describes a comprehensive FPP software tool called PhaseWareTM that allows novice to experienced users to perform pre-processing of fringe patterns, phase retrieval, phase unwrapping, and finally post-processing. The sequential process of acquiring fringe patterns from an object is necessary to sample the surface densely enough to accurately estimate surface profiles. Sequential fringe acquisition performs poorly if the object is in motion between fringe projections. To overcome this limitation, we developed a novel method of FPP called multispectral fringe projection profilometry (MFPP), where multiple fringe patterns are composited into a multispectral illumination pattern and a single multispectral camera is used to capture the frame. Chapter 3 introduces this new technique and shows how it can be used to perform 3D profilometry at video frame rates. Although the first attempt at MFPP significantly improved acquisition speed, it did not fully satisfy the condition for temporal phase retrieval, which requires at least three phase-shifted fringe patterns to characterize a surface. To overcome this limitation, Chapter 4 introduces an enhanced version of MFPP that utilized a specially designed multispectral illuminator to simultaneously project four p/2 phase-shifted fringe patterns onto an object. Combined with spectrally matched multispectral imaging, the refined MFPP method resulted in complete data for temporal phase retrieval using only a single camera exposure, thereby maintaining the high sampling speed for profilometry of moving objects. In conclusion, MFPP overcomes the limitations of sequential sampling imposed by FPP with temporal phase extraction without sacrificing data quality or accuracy of the reconstructed surface profiles. Since MFPP utilizes no moving parts and is based on MEMS technology, it is applicable to miniaturization for use in mobile devices and may be useful for space-constrained applications such as robotic surgery. Fringe Projection Profilometry (FPP) is a popular method for non-contact optical surface measurements such as motion tracking. The technique derives 3D surface maps from phase maps estimated from the distortions of fringe patterns projected onto the surface of the object. To estimate surface profiles accurately, sequential acquisition of fringe patterns is required; however, sequential fringe projection and acquisition perform poorly if the object is in motion during the projection. To overcome this limitation, we developed a novel method of FPP maned multispectral fringe projection profilometry (MFPP). The proposed method provides multispectral illumination patterns using a multispectral filter array (MFA) to generate multiple fringe patterns from a single illumination and capture the composite pattern using a single multispectral camera. Therefore, a single camera acquisition can provide multiple fringe patterns, and this directly increases the speed of imaging by a factor equal to the number of fringe patterns included in the composite pattern. Chapter 3 introduces this new technique and shows how it can be used to perform 3D profilometry at video frame rates. The first attempt at MFPP significantly improved acquisition speed by a factor of eight by providing eight different fringe patterns in four different directions, which permits the system to detect more morphological details. However, the phase retrieval algorithm used in this method was based on the spatial phase stepping process that had a few limitations, including high sensitive to the quality of the fringe patterns and being a global process, as it spreads the effect of the noisy pixels across the entire result. To overcome this limitation, Chapter 4 introduces an enhanced version of MFPP that utilized a specially designed multispectral illuminator to simultaneously project four p/2 phase-shifted fringe patterns onto an object. Combined with a spectrally matched multispectral camera, the refined MFPP method provided the needed data for the temporal phase retrieval algorithm using only a single camera exposure. Thus, it delivers high accuracy and pixel-wise measurement (thanks to the temporal phase stepping algorithms) while maintaining a high sampling rate for profilometry of moving objects. In conclusion, MFPP overcomes the limitations of sequential sampling imposed by FPP with temporal phase extraction without sacrificing data quality or accuracy of the reconstructed surface profiles. Since MFPP utilizes no moving parts and is based on MEMS technology, it is applicable to miniaturization for use in mobile devices and may be useful for space-constrained applications such as robotic surgery

Acquisition of 3D shapes of moving objects using fringe projection profilometry

Three-dimensional (3D) shape measurement for object surface reconstruction has potential applications in many areas, such as security, manufacturing and entertainment. As an effective non-contact technique for 3D shape measurements, fringe projection profilometry (FPP) has attracted significant research interests because of its high measurement speed, high measurement accuracy and ease to implement. Conventional FPP analysis approaches are applicable to the calculation of phase differences for static objects. However, 3D shape measurement for dynamic objects remains a challenging task, although they are highly demanded in many applications. The study of this thesis work aims to enhance the measurement accuracy of the FPP techniques for the 3D shape of objects subject to movement in the 3D space. The 3D movement of objects changes not only the position of the object but also the height information with respect to the measurement system, resulting in motion-induced errors with the use of existing FPP technology. The thesis presents the work conducted for solutions of this challenging problem

Accurate measurement of Cn2 profile with Shack-Hartmann data

The precise reconstruction of the turbulent volume is a key point in the development of new-generation Adaptive Optics systems. We propose a new Cn2 profilometry method named CO-SLIDAR (COupled Slope and scIntillation Detection And Ranging), that uses correlations of slopes and scintillation indexes recorded on a Shack-Hartmann from two separated stars. CO-SLIDAR leads to an accurate Cn2 retrieval for both low and high altitude layers. Here, we present an end-to-end simulation of the Cn2 profile measurement. Two Shack-Hartmann geometries are considered. The detection noises are taken into account and a method to subtract the bias is proposed. Results are compared to Cn2 profiles obtained from correlations of slopes only or correlations of scintillation indexes only.Comment: 10 pages, 8 figures, SPIE Conference "Astronomical Telescopes and Instrumentation" 2012, Amsterdam, paper 8447-19

A spatial algorithm to reduce phase wraps from two dimensional signals in fringe projection profilometry

© 2015 Elsevier Ltd. All rights reserved. In this paper, we present a novel algorithm to reduce the number of phase wraps in two dimensional signals in fringe projection profilometry. The technique operates in the spatial domain, and achieves a significant computational saving with regard to existing methods based on frequency shifting. The method works by estimating the modes of the first differences distribution in each axial direction. These are used to generate a tilted plane, which is subtracted from the entire phase map. Finally, the result is re-wrapped to obtain a phase map with fewer wraps. The method may be able to completely eliminate the phase wraps in many cases, or can achieve a significant phase wrap reduction that helps the subsequent unwrapping of the signal. The algorithm has been exhaustively tested across a large number of real and simulated signals, showing similar results compared to approaches operating in the frequency domain, but at significantly lower running times