Label enhanced and patch based deep learning for phase retrieval from single frame fringe pattern in fringe projection 3D measurement.

We propose a label enhanced and patch based deep learning phase retrieval approach which can achieve fast and accurate phase retrieval using only several fringe patterns as training dataset. To the best of our knowledge, it is the first time that the advantages of the label enhancement and patch strategy for deep learning based phase retrieval are demonstrated in fringe projection. In the proposed method, the enhanced labeled data in training dataset is designed to learn the mapping between the input fringe pattern and the output enhanced fringe part of the deep neural network (DNN). Moreover, the training data is cropped into small overlapped patches to expand the training samples for the DNN. The performance of the proposed approach is verified by experimental projection fringe patterns with applications in dynamic fringe projection 3D measurement.

[1]  Liang Zhang,et al.  Fringe pattern analysis using deep learning , 2018, Advanced Photonics.

[2]  Guohua Gu,et al.  Micro deep learning profilometry for high-speed 3D surface imaging , 2019, Optics and Lasers in Engineering.

[3]  Demetrio Labate,et al.  Shearlet Smoothness Spaces , 2013 .

[4]  Michael Unser,et al.  Deep Convolutional Neural Network for Inverse Problems in Imaging , 2016, IEEE Transactions on Image Processing.

[5]  Xinjun Zhu,et al.  Variational image decomposition for automatic background and noise removal of fringe patterns. , 2013, Optics letters.

[6]  Jianlin Zhao,et al.  One-step robust deep learning phase unwrapping. , 2019, Optics express.

[7]  Jing Xu,et al.  High-accuracy, high-speed 3D structured light imaging techniques and potential applications to intelligent robotics , 2017, International Journal of Intelligent Robotics and Applications.

[8]  Limei Song,et al.  Assessment of Fringe Pattern Decomposition with a Cross-Correlation Index for Phase Retrieval in Fringe Projection 3D Measurements , 2018, Sensors.

[9]  Anand Asundi,et al.  Comparison of Fourier transform, windowed Fourier transform, and wavelet transform methods for phase extraction from a single fringe pattern in fringe projection profilometry , 2010 .

[10]  Song Zhang,et al.  High-speed 3D shape measurement with structured light methods: A review , 2018, Optics and Lasers in Engineering.

[11]  Song Zhang,et al.  Absolute phase retrieval methods for digital fringe projection profilometry: A review , 2018 .

[12]  Guohai Situ,et al.  eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction. , 2018, Optics express.

[13]  Francis Lilley,et al.  Spatial fringe pattern analysis using the two-dimensional continuous wavelet transform employing a cost function. , 2007, Applied optics.

[14]  Hui Liu,et al.  Structured-Light Based 3D Reconstruction System for Cultural Relic Packaging , 2018, Sensors.

[15]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Chen Tang,et al.  A 3D shape retrieval method for orthogonal fringe projection based on a combination of variational image decomposition and variational mode decomposition , 2016 .

[17]  Xiang Zhou,et al.  Morphological operation-based bi-dimensional empirical mode decomposition for automatic background removal of fringe patterns. , 2012, Optics express.

[18]  Rama Krishna Sai Subrahmanyam Gorthi,et al.  PhaseNet: A Deep Convolutional Neural Network for Two-Dimensional Phase Unwrapping , 2019, IEEE Signal Processing Letters.

[19]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[20]  Qian Chen,et al.  Phase shifting algorithms for fringe projection profilometry: A review , 2018, Optics and Lasers in Engineering.

[21]  Sai Siva Gorthi,et al.  Fringe projection techniques: Whither we are? , 2010 .

[22]  Michael Unser,et al.  High-Quality Parallel-Ray X-Ray CT Back Projection Using Optimized Interpolation , 2017, IEEE Transactions on Image Processing.

[23]  Yibo Zhang,et al.  Phase recovery and holographic image reconstruction using deep learning in neural networks , 2017, Light: Science & Applications.

[24]  Xinjun Zhu,et al.  Shearlet transform for phase extraction in fringe projection profilometry with edges discontinuity , 2016 .

[25]  Linlin Wang,et al.  Phase retrieval from single frame projection fringe pattern with variational image decomposition , 2014 .

[26]  M. Takeda,et al.  Fourier transform profilometry for the automatic measurement of 3-D object shapes. , 1983, Applied optics.

[27]  Junchao Zhang,et al.  Phase unwrapping in optical metrology via denoised and convolutional segmentation networks. , 2019, Optics express.