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RGB条纹投影轮廓术的傅里叶分析及针对串扰失真的稳健相位解调方法

Fourier analysis of RGB fringe-projection profilometry and robust phase-demodulation methods against crosstalk distortion.

作者信息

Padilla Moises, Servin Manuel, Garnica Guillermo

出版信息

Opt Express. 2016 Jul 11;24(14):15417-28. doi: 10.1364/OE.24.015417.

Abstract

In this paper, we apply the frequency transfer function formalism to analyze the red, green and blue (RGB) phase-shifting fringe-projection profilometry technique. The phase-shifted patterns in RGB fringe projection are typically corrupted by crosstalk because the sensitivity curves of most projection-recording systems overlap. This crosstalk distortion needs to be compensated in order to obtain high quality measurements. We study phase-demodulation methods for null/mild, moderate, and severe levels of RGB crosstalk. For null/mild crosstalk distortion, we can estimate the searched phase-map using Bruning's 3-step phase-shifting algorithm (PSA). For moderate crosstalk, the recorded data is usually preprocessed before feeding it into the PSA; alternatively, in this paper we propose a computationally more efficient approach, which combines linear crosstalk compensation with the phase-demodulation algorithm. For severe RGB crosstalk, we expect non-sinusoidal fringes' profiles (distorting harmonics) and a significant uncertainty on the linear crosstalk calibration (which produces pseudo-detuning error). Analyzing these distorting phenomena, we conclude that squeezing interferometry is the most robust demodulation method for RGB fringe-projection techniques. Finally, we support our conclusions with numerical simulations and experimental results.

摘要

在本文中,我们应用频率传递函数形式来分析红、绿、蓝(RGB)相移条纹投影轮廓测量技术。RGB条纹投影中的相移图案通常会因串扰而失真,因为大多数投影记录系统的灵敏度曲线会相互重叠。为了获得高质量的测量结果,需要对这种串扰失真进行补偿。我们研究了针对RGB串扰的零/轻度、中度和重度水平的相位解调方法。对于零/轻度串扰失真,我们可以使用布鲁宁三步相移算法(PSA)来估计搜索到的相位图。对于中度串扰,记录的数据通常在输入PSA之前进行预处理;或者,在本文中我们提出了一种计算效率更高的方法,该方法将线性串扰补偿与相位解调算法相结合。对于严重的RGB串扰,我们预计非正弦条纹的轮廓(谐波失真)以及线性串扰校准存在显著不确定性(会产生伪失谐误差)。通过分析这些失真现象,我们得出结论,挤压干涉测量法是RGB条纹投影技术中最稳健的解调方法。最后,我们通过数值模拟和实验结果来支持我们的结论。

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