Multi-frequency inverse-phase fringe projection profilometry for nonlinear phase error compensation

Lei, Zhenkun; Wang, Chunli; Zhou, Changli

doi:10.1016/j.optlaseng.2014.09.018

Cited by 66 publications

(17 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When such measurements are used to inform decisions regarding part acceptance, maintenance, or usage, it is important to understand and quantify uncertainties in the measurement process prior to implementation. Potential sources for measurement uncertainty in the DFP method include projector gamma nonlinearity [9][10][11], projector and camera quantization effects [12][13][14], and pixel intensity noise [15][16][17][18]. Substantial work has been made to quantify and mitigate the height profile measurement error caused by non-linear projector gamma [19][20][21][22] and other publications address measurement uncertainty and mitigation caused by projector and camera quantization [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…It has motivated development of new phase-shifting algorithms [17,18], and has been reported to dominate the error contributions from camera and projection quantization error [29]. Even with the development of nonlinear gamma error reduction, measurement uncertainty contributions from noise in the pixel field is unavoidable [15]. However, to the authors' knowledge, no work has been published which explicitly provides a forward analytical model of the measurement height uncertainty caused by phase noise.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A probability density function model describing height estimation uncertainty due to image pixel intensity noise in digital fringe projection measurements

O’Dowd

Wachtor

Todd

2021

Optics and Lasers in Engineering

View full text Add to dashboard Cite

Digital fringe projection is a surface-profiling technique used for highly accurate noncontact measurements. As with any measurement technique, a variety of sources degrade to the measurement accuracy of the method. This paper presents an analytically-derived probability density function that explicitly models the surface height measurement error due to inevitable phase measurement error, and it includes the specific case of pixel noise inducing the phase measurement error that ultimately leads to the height estimation error. The accuracy of the model was validated through Monte-Carlo simulations of resultant height distributions subject to arbitrarily correlated pixel intensity noise and experimental digital fringe projection measurements where the pixel-by-pixel height uncertainty estimations were compared to the predictions of the derived model.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A probability density function model describing height estimation uncertainty due to image pixel intensity noise in digital fringe projection measurements

O’Dowd

Wachtor

Todd

2021

Optics and Lasers in Engineering

View full text Add to dashboard Cite

show abstract

“…Several sources of error effect the accuracy of DFP measurement, including nonlinear projector gamma [17][18][19], phase-to-height calibration error [20,21], and pixel intensity noise caused by fluctuation in environmental lighting during DFP measurement and electronic noise within the camera and projector [22][23][24][25]. The current work is focused on quantifying phase error converted from pixel intensity noise, a source of error affecting a wide range of profiling techniques [23].…”

Section: Introductionmentioning

confidence: 99%

A model for describing phase-converted image intensity noise in digital fringe projection techniques

O’Dowd

Wachtor

Todd

2020

Optics and Lasers in Engineering

View full text Add to dashboard Cite

Digital fringe projection is a surface-profiling technique that is gaining popularity due to the increasing availability and quality of low-cost projection equipment and digital cameras. Noise in the pixel field of imaged targets induces error in the reconstructed phase and ultimately the surface profile measurement. In this paper, we present an approximate analytical probability density function for the estimated phase given an arbitrarily-correlated Gaussian pixel noise structure. This probability density function can be used to estimate the single point phase measurement uncertainty from easily obtainable pixel intensity noise statistics. We confirm the accuracy of the new model by comparing it to a Monte-Carlo simulation of the phase distribution. A complimentary graphics model is proposed which simulates the physical process of full-field phase measurement using a pin-hole camera model and three-dimensional point clouds of the measurement surface, allowing for another level of model verification.

show abstract

“…Three-dimensional (3D) surface profiles can be obtained by using non-contact and highaccuracy fringe projection profilometry (FPP), which finds a wide variety of applications such as biological and medical imaging, computer vision and so on [1][2][3]. It is well known that the FPP heavily relies on the recovered phase, so that phase measuring profilometry (PMP) is generally a crucial process.…”

Section: Introductionmentioning

confidence: 99%

Precise and fast phase wraps reduction in fringe projection profilometry

Wang

Zhou

et al. 2017

Journal of Modern Optics

Self Cite

View full text Add to dashboard Cite

The number of phase wraps in 2D wrapped phase map can be completely eliminated, or greatly reduced by frequency shifting. But the wraps usually cannot be optimally reduced using the conventional fast Fourier transform (FFT) because the spectrum can be shifted only by an integer number in the frequency domain. In order to completely eliminate the phase wraps or achieve a significant phase wrap reduction, in this paper, we propose a fast and precise two-step method for phase wraps reduction, which uses the iterative local discrete Fourier transform (DFT) to determine the sub-pixel spectral peak location and the frequency shifting algorithm that operates in spatial domain to reduce the number of phase wraps. Firstly, an initial estimate of the frequency peak is obtain by FFT, then the sub-pixel spectral peak with high resolution is determined by iteratively upsampling the local DFT around the initial spectral peak location, further the non-integer frequency shifting in spatial domain is realized to eliminate or reduce the number of phase wraps. Finally, simulations and experiments are conducted to prove the validity of the proposed method. The results demonstrates the proposed method's superb computing efficiency, high resolution and overall performance.

show abstract

Multi-frequency inverse-phase fringe projection profilometry for nonlinear phase error compensation

Cited by 66 publications

References 22 publications

A probability density function model describing height estimation uncertainty due to image pixel intensity noise in digital fringe projection measurements

A probability density function model describing height estimation uncertainty due to image pixel intensity noise in digital fringe projection measurements

A model for describing phase-converted image intensity noise in digital fringe projection techniques

Precise and fast phase wraps reduction in fringe projection profilometry

Contact Info

Product

Resources

About