Deep learning approaches for unwrapping phase images with steep spatial gradients: a simulation

We explore different deep learning-based approaches to solve the problem of phase unwrapping in objects with high spatial gradients, which is applicable to many fields in medicine, biology and remote sensing. We simulate data with high spatial gradients to compare the quality of the solution and the runtime obtained when addressing this problem either as an inverse problem or as a semantic segmentation problem.

[1]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[2]  K. Nugent,et al.  Refractive-index profiling of optical fibers with axial symmetry by use of quantitative phase microscopy. , 2002, Optics letters.

[3]  K Itoh,et al.  Analysis of the phase unwrapping algorithm. , 1982, Applied optics.

[4]  Roger M. Groves,et al.  Statistically guided improvements in speckle phase discontinuity predictions by machine learning systems , 2013 .

[5]  Paulo J. G. Lisboa,et al.  Phase unwrapping in 3-D shape measurement using artificial neural networks , 1997 .

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

[7]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Pinhas Girshovitz,et al.  Optical phase nanoscopy in red blood cells using low-coherence spectroscopy , 2012, Journal of biomedical optics.

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

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

[11]  Richard Frayne,et al.  Hemodynamic alterations measured with phase-contrast MRI in a giant cerebral aneurysm treated with a flow-diverting stent , 2016, Radiology case reports.

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

[13]  David R. Burton,et al.  A NEURAL NETWORK APPROACH TO THE PHASE UNWRAPPING PROBLEM IN FRINGE ANALYSIS , 1996 .

[14]  Natan T. Shaked,et al.  Quantitative Analysis of Biological Cells Using Digital Holographic Microscopy , 2011 .

[15]  Chen Tang,et al.  Denoising by coupled partial differential equations and extracting phase by backpropagation neural networks for electronic speckle pattern interferometry. , 2007, Applied optics.

[16]  Anzhi He,et al.  Phase unwrapping by a random artificial neural network , 1997, Optics & Photonics.

[17]  Mario Costantini,et al.  A three-dimensional phase unwrapping algorithm for processing of multitemporal SAR interferometric measurements , 2002, IEEE International Geoscience and Remote Sensing Symposium.

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

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

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

[21]  J. F. Greenleaf,et al.  Magnetic resonance elastography: Non-invasive mapping of tissue elasticity , 2001, Medical Image Anal..

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

[23]  Shuai Li,et al.  Lensless computational imaging through deep learning , 2017, ArXiv.