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时间相位展开技术的相位展开结果具有良好的鲁棒性和准确性,也可用于测量非连续表面,因此应用范围较广。虽然空间相位展开技术测量精度相对时间相位展开技术较差一些,但是具有测量速度快的优势,可以有效用于动态物体测量。为了避免传统时间和空间相位展开技术中存在的问题,达到优势互补,结合时间和空间方法在测量时间和测量精度上取折衷,提出了时空相位展开技术。该方法测量结果达到比时间相位展开速度快,比空间相位展开精度高,但是测量精度不及时间相位展开,测量速度不及空间相位展开。为了克服传统的时间和空间相位展开技术的一些限制,基于几何约束的相位展开技术被相继提出。利用测量仪器与相位的空间约束特性实现相位展开,测量速率得到了有效的提升。但是该方法的测量深度范围具有一定的局限性,此外也会增加测量成本。引入其他新的约束条件用于相位展开的方法也在不断的探索过程中。计算机技术的发展使得深度学习越来越广泛地应用到光学计量中[16, 127],深度学习可以克服传统时间相位展开技术和空间相位展开技术的不足,实现快速测量的同时保证一定的精度。但是对于不同的基于深度学习的相位展开技术也存在着一定的不足之处,比如:需要合适的数据集、训练合适的网络模型、可靠性不稳定、硬件设施要求高、需额外增加预处理和后处理步骤等。相关学者们也对部分相位展开技术做了一定的分类和对比[13, 15, 128]。为了更好地对前述的相位展开技术进行说明,文中对几种典型的相位展开技术的性能进行了总结,如表1所示。
Method classification Typical method Reference Number of patterns Measured
speedNoise resistance Accuracy TPU PU-GC [33, 36-37, 39, 41] Medium High High Low MFPU [13, 48, 52, 54-55, 61] High Low High High MWPU [15, 62- 63] High Low Low Medium NTPU [64-66] Medium Medium Medium High SPU QGPU [68, 70, 73-74, 77, 79] Low High Low Low BCPU [84, 87] Low High High Low
PU-DLPhaseNet [97-98] Medium High Medium Medium TriNet [103] Medium High High Medium MCDL [104] Low High Medium High BNN [109] Low High Medium Medium DL-TPU [104-106] Medium High High High DCFPP [113] Low High Medium High MFFN [115] Low High Medium High LPTPU-DL [116] Low High Medium High U-DL [117] Low High Medium High
OPUT-SBE [120] Medium Medium Medium Medium GCPU [123-124] Medium High Medium High PCPU [126] Low High Low Medium Table 1. Comparison of typical phase unwrapping methods
从所需的投影条纹图案数目、测量速度、抗噪性能和计算精度方面进行了比较。对表1中的相位展开技术进行以下讨论:
1)相位展开过程中所需的投影条纹图案数目。由于条纹图案上携带着空间和相位信息,对于时间相位展开而言,数目越多则测量精度越高;而空间相位展开技术则所需的图案数目较少。基于深度学习的相位展开技术和其他相位展开技术以及用于高速测量需求的相位展开技术都在趋向于减少条纹图案数目。
2)测量速度的影响因素。所需条纹图案的数目(包括是否需要增加额外的条纹投案)、测量所需的时间、计算复杂度、相位展开过程是否需要预处理和后处理步骤等,不同的方法会从一个或多个因素来加快测量速度。针对静态物体对测量速度没有明确的限制,而对于高动态物体则需要对测量速度提出更高的要求,尤其是对测量过程所需要投影条纹的数目提出了更高的要求。
3)抗噪性能是决定测量精确度和可靠性的关键因素。实际测量环境中的噪声因素不可避免,为了尽可能地减少噪声对测量结果的影响,从方法原理的抗干扰性和误差扩散、采用的条纹投影图案对噪声的抑制作用和对方法的改进中通过预处理、后处理或者滤波等方法实现增强相位展开的抗噪性能。在各类相位展开技术中,空间相位展开技术的抗噪性相对较差,从原理上空间相位展开依赖于相邻像素实现相位展开,因此对噪声较为敏感。
4)相位展开精度是决定三维测量结果精度的关键因素。针对测量精度要求高的场景,可以选择时间相位展开技术中的多频相位展开实现高精度测量,基于深度学习的相位展开技术也在一定程度上达到高精度要求。
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