One-step robust deep learning phase unwrapping.

Phase unwrapping is an important but challenging issue in phase measurement. Even with the research efforts of a few decades, unfortunately, the problem remains not well solved, especially when heavy noise and aliasing (undersampling) are present. We propose a database generation method for phase-type objects and a one-step deep learning phase unwrapping method. With a trained deep neural network, the unseen phase fields of living mouse osteoblasts and dynamic candle flame are successfully unwrapped, demonstrating that the complicated nonlinear phase unwrapping task can be directly fulfilled in one step by a single deep neural network. Excellent anti-noise and anti-aliasing performances outperforming classical methods are highlighted in this paper.

[1]  G. T. Reid,et al.  Interferogram Analysis: Digital Fringe Pattern Measurement Techniques , 1994 .

[2]  Edmund Y. Lam,et al.  Learning-based nonparametric autofocusing for digital holography , 2018 .

[3]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[4]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  David L. Fried,et al.  Least-square fitting a wave-front distortion estimate to an array of phase-difference measurements , 1977 .

[6]  Shouyu Wang,et al.  Real time quantitative phase microscopy based on single-shot transport of intensity equation (ssTIE) method , 2016 .

[7]  Stephan Antholzer,et al.  Deep learning for photoacoustic tomography from sparse data , 2017, Inverse problems in science and engineering.

[8]  Guohai Situ,et al.  Deep-learning-based ghost imaging , 2017, Scientific Reports.

[9]  A. Asundi,et al.  Noninterferometric single-shot quantitative phase microscopy. , 2013, Optics letters.

[10]  George Barbastathis,et al.  Imaging through glass diffusers using densely connected convolutional networks , 2017, Optica.

[11]  Natan T. Shaked,et al.  Phase Unwrapping Using Residual Neural Networks , 2018 .

[12]  Naoto Hoshikawa,et al.  Digital holographic particle volume reconstruction using a deep neural network , 2019, Applied optics.

[13]  M. Teague.  Deterministic phase retrieval: a Green’s function solution , 1983 .

[14]  Xin Yuan,et al.  Parallel lensless compressive imaging via deep convolutional neural networks. , 2018, Optics express.

[15]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

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

[17]  Qian Kemao,et al.  Two-dimensional windowed Fourier transform for fringe pattern analysis: Principles, applications and implementations , 2007 .

[18]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[19]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[21]  Qian Chen,et al.  Transport-of-intensity phase imaging using Savitzky-Golay differentiation filter--theory and applications. , 2013, Optics express.

[22]  C. Werner,et al.  Satellite radar interferometry: Two-dimensional phase unwrapping , 1988 .

[23]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Sepp Hochreiter,et al.  Untersuchungen zu dynamischen neuronalen Netzen , 1991 .

[25]  Rene A. Claus,et al.  Transport of Intensity phase imaging by intensity spectrum fitting of exponentially spaced defocus planes. , 2014, Optics express.

[26]  Mark D. Pritt,et al.  Least-squares two-dimensional phase unwrapping using FFT's , 1994, IEEE Trans. Geosci. Remote. Sens..

[27]  Tomi Pitkäaho,et al.  Focus prediction in digital holographic microscopy using deep convolutional neural networks. , 2019, Applied optics.

[28]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

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

[30]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[31]  Jianlin Zhao,et al.  Quantitative phase microscopy for cellular dynamics based on transport of intensity equation. , 2018, Optics express.

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

[33]  Joydeep Ghosh,et al.  Two-dimensional phase unwrapping using neural networks , 2000, 4th IEEE Southwest Symposium on Image Analysis and Interpretation.

[34]  A. Asundi,et al.  High-speed transport-of-intensity phase microscopy with an electrically tunable lens. , 2013, Optics express.

[35]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[36]  Justin Lee,et al.  Lensless computational imaging through deep learning , 2017, ArXiv.

[37]  Kyoung Mu Lee,et al.  Accurate Image Super-Resolution Using Very Deep Convolutional Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  J. Tanida,et al.  Learning-based imaging through scattering media. , 2016, Optics express.

[39]  Gary H. Glover,et al.  Phase unwrapping of MR phase images using Poisson equation , 1995, IEEE Trans. Image Process..

[40]  Leslie Ying,et al.  Accelerating magnetic resonance imaging via deep learning , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[41]  K. Nugent,et al.  Quantitative optical phase microscopy. , 1998, Optics letters.

[42]  R. Hudgin.  Wave-front reconstruction for compensated imaging , 1977 .

[43]  Jianglei Di,et al.  Deep learning-based object classification through multimode fiber via a CNN-architecture SpeckleNet. , 2018, Applied optics.

[44]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[45]  Qican Zhang,et al.  Quality-guided phase unwrapping technique: comparison of quality maps and guiding strategies. , 2011, Applied optics.

[46]  G. Barbastathis,et al.  Transport of intensity phase imaging in a volume holographic microscope. , 2010, Optics letters.

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

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

[49]  Ulugbek Kamilov,et al.  Efficient and accurate inversion of multiple scattering with deep learning , 2018, Optics express.

[50]  Yibo Zhang,et al.  Deep Learning Microscopy , 2017, ArXiv.

[51]  Daniel Rueckert,et al.  Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[52]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[53]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[54]  J. M. Huntley,et al.  Temporal phase-unwrapping algorithm for automated interferogram analysis. , 1993, Applied optics.