Unifying temporal phase unwrapping framework using deep learning

被引:23
|
作者
Guo, Xinming [1 ,2 ,3 ]
Li, Yixuan [1 ,2 ,3 ]
Qian, Jiaming [1 ,2 ,3 ]
Che, Yuxuan [1 ,2 ,3 ]
Zuo, Chao [1 ,2 ,3 ]
Chen, Qian [1 ,3 ]
Lam, Edmund Y. [4 ]
Wang, Huai [5 ]
Feng, Shijie [1 ,2 ,3 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Elect & Opt Engn, Smart Computat Imaging Lab SCILab, Nanjing 210094, Jiangsu, Peoples R China
[2] Nanjing Univ Sci & Technol, Smart Computat Imaging Res Inst SCIRI, Nanjing 210019, Jiangsu, Peoples R China
[3] Jiangsu Key Lab Spectral Imaging & Intelligent Sen, Nanjing 210094, Jiangsu, Peoples R China
[4] Univ Hong Kong, Dept Elect & Elect Engn, Pokfulam Rd, Hong Kong, Peoples R China
[5] Suzhou Abham Intelligence Technol Co Ltd, Suzhou 215000, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
3-DIMENSIONAL SHAPE MEASUREMENT; FOURIER-TRANSFORM PROFILOMETRY; FRINGE PROJECTION PROFILOMETRY; ALGORITHM; INTERFEROMETRY; OBJECTS; RANGE; IMAGE; LASER;
D O I
10.1364/OE.488597
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Temporal phase unwrapping (TPU) is significant for recovering an unambiguous phase of discontinuous surfaces or spatially isolated objects in fringe projection profilometry. Generally, temporal phase unwrapping algorithms can be classified into three groups: the multi-frequency (hierarchical) approach, the multi-wavelength (heterodyne) approach, and the number-theoretic approach. For all of them, extra fringe patterns of different spatial frequencies are required for retrieving the absolute phase. Due to the influence of image noise, people have to use many auxiliary patterns for high-accuracy phase unwrapping. Consequently, image noise limits the efficiency and the measurement speed greatly. Further, these three groups of TPU algorithms have their own theories and are usually applied in different ways. In this work, for the first time to our knowledge, we show that a generalized framework using deep learning can be developed to perform the TPU task for different groups of TPU algorithms. Experimental results show that benefiting from the assistance of deep learning the proposed framework can mitigate the impact of noise effectively and enhance the phase unwrapping reliability significantly without increasing the number of auxiliary patterns for different TPU approaches. We believe that the proposed method demonstrates great potential for developing powerful and reliable phase retrieval techniques.
引用
收藏
页码:16659 / 16675
页数:17
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