Phase unwrapping in optical metrology via denoised and convolutional segmentation networks

Zhang, Junchao; Tian, Xiaobo; Shao, Jianbo; Luo, Haibo; Liang, Rongguang

doi:10.1364/oe.27.014903

Cited by 110 publications

(54 citation statements)

References 19 publications

(32 reference statements)

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“…Nevertheless, this network is not publicly available, and thus cannot be tested on other types of biological cells. The concept of deep-learning phase unwrappers was recently demonstrated for other applications as well, including 2-D phase unwrapping in optical metrology [41,42] and lens-free imaging [43], as well as temporal phase unwrapping in fringe projection profilometry [44].…”

Section: Introductionmentioning

confidence: 99%

PhUn-Net: ready-to-use neural network for unwrapping quantitative phase images of biological cells

Dardikman-Yoffe

Roitshtain

Mirsky

et al. 2020

Biomed. Opt. Express

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We present a deep-learning approach for solving the problem of 2π phase ambiguities in two-dimensional quantitative phase maps of biological cells, using a multi-layer encoderdecoder residual convolutional neural network. We test the trained network, PhUn-Net, on various types of biological cells, captured with various interferometric setups, as well as on simulated phantoms. These tests demonstrate the robustness and generality of the network, even for cells of different morphologies or different illumination conditions than PhUn-Net has been trained on. In this paper, for the first time, we make the trained network publicly available in a global format, such that it can be easily deployed on every platform, to yield fast and robust phase unwrapping, not requiring prior knowledge or complex implementation. By this, we expect our phase unwrapping approach to be widely used, substituting conventional and more time-consuming phase unwrapping algorithms.

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Section: Introductionmentioning

confidence: 99%

PhUn-Net: ready-to-use neural network for unwrapping quantitative phase images of biological cells

Dardikman-Yoffe

Roitshtain

Mirsky

et al. 2020

Biomed. Opt. Express

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“…Recently deep learning methods have been proposed as an alternative approach for phase unwrapping to improve the speed and accuracy [ 13 , 14 , 15 , 16 , 17 , 18 ]. Some of the proposed methods are based on the use of a residual neural network [ 13 , 17 ] to directly perform the unwrapping task.…”

Section: Introductionmentioning

confidence: 99%

“…These methods can obtain the unwrapped phase directly, but with relatively large errors. Others are based on the segmentation neural network [ 14 , 15 ], where the strategy is to segment the wrapped phase and label each segment with an integer, and then train the neural network to label each segment. While those deep learning based phase unwrapping methods have different degrees of success in their targeted fields, no general deep learning based method is available to address the phase unwrapping problems in phase-shift fringe projection 3D imaging.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging

Liang

Zhang

Shao

et al. 2020

Sensors

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Phase unwrapping is a very important step in fringe projection 3D imaging. In this paper, we propose a new neural network for accurate phase unwrapping to address the special needs in fringe projection 3D imaging. Instead of labeling the wrapped phase with integers directly, a two-step training process with the same network configuration is proposed. In the first step, the network (network I) is trained to label only four key features in the wrapped phase. In the second step, another network with same configuration (network II) is trained to label the wrapped phase segments. The advantages are that the dimension of the wrapped phase can be much larger from that of the training data, and the phase with serious Gaussian noise can be correctly unwrapped. We demonstrate the performance and key features of the neural network trained with the simulation data for the experimental data.

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“…Specifically for the optical metrology techniques, CNNs have been applied as a phase demodulation from a single fringe pattern in projection profilometry [27], as a phase and amplitude reconstructor from a single hologram intensity pattern in holography [28], as an estimator of depth position without multiple diffraction calculations in digital holography [29], and as an optical fringe pattern denoising method in interferometry [30]. CNNs have also been applied in digital holographic interferometry, including new phase unwrapping methods, e.g., Spoorthi et al [31] proposed a phase unwrapping method using the wrapped phase as input and wrap-count as a semantic label, Zhang et al [32] presented a phase unwrapping method based on a semantic segmentation algorithm, and Zhang et al [33] generated the unwrapped phase from the combination of a denoised wrapped phase and a corrected integral multiple.…”

Section: Introductionmentioning

confidence: 99%

Digital Holographic Interferometry without Phase Unwrapping by a Convolutional Neural Network for Concentration Measurements in Liquid Samples

et al. 2020

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Convolutional neural networks (CNNs) and digital holographic interferometry (DHI) can be combined to improve the calculation efficiency and to simplify the procedures of many DHI applications. In DHI, for the measurements of concentration differences between liquid samples, two or more holograms are compared to find the difference phases among them, and then to estimate the concentration values. However, liquid samples with high concentration difference values are difficult to calculate using common phase unwrapping methods as they have high spatial frequencies. In this research, a new method to skip the phase unwrapping process in DHI, based on CNNs, is proposed. For this, images acquired by Guerrero-Mendez et al. (Metrology and Measurement Systems 24, 19–26, 2017) were used to train the CNN, and a multiple linear regression algorithm was fitted to estimate the concentration values for liquid samples. In addition, new images were recorded to evaluate the performance of the proposed method. The proposed method reached an accuracy of 0.0731%, and a precision of ±0.0645. The data demonstrated a high repeatability of 0.9986, with an operational range from 0.25 gL−1 to 1.5 gL−1. The proposed method was performed with liquid samples in a cylindrical glass.

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Phase unwrapping in optical metrology via denoised and convolutional segmentation networks

Cited by 110 publications

References 19 publications

PhUn-Net: ready-to-use neural network for unwrapping quantitative phase images of biological cells

PhUn-Net: ready-to-use neural network for unwrapping quantitative phase images of biological cells

Deep Convolutional Neural Network Phase Unwrapping for Fringe Projection 3D Imaging

Digital Holographic Interferometry without Phase Unwrapping by a Convolutional Neural Network for Concentration Measurements in Liquid Samples

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