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

Dardikman, Gili; Turko, Nir A.; Shaked, Natan T.

doi:10.1109/icsee.2018.8646266

Cited by 6 publications

(10 citation statements)

References 23 publications

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“…We believe this is still the better choice of implementation, since if we had chosen a semantic-segmentation architecture, which simply outputs an integer for each pixel (i.e. the quotient), the result would suffer from severe local errors [37]. Moreover, the separation of the segmentation step from the network allows each end-user to choose whether or not they want to apply it, thus effectively choosing between local and global minimization.…”

Section: Discussionmentioning

confidence: 99%

“…For example, in digital holographic microscopy, this noise includes speckle noise, shot noise, and readout noise, as well as typical aberrations caused by an inhomogeneous illumination. Viewing the problem of reconstructing the original phase from the recorded phase as an inverse problem, as we have found to be the optimal approach [37], actually means trying to find the pixel values that globally minimize the error. This is similar to minimum-norm methods, where we try to minimize the norm of the differences between the gradient of the intermediate phase reconstruction and that of the measured phase [14].…”

Section: Methodsmentioning

confidence: 99%

“…Harnessing recent advances, both in software and in hardware, we have recently explored different deep learning-based approaches for solving the problem of phase unwrapping in objects with high spatial gradients (not meeting Itoh's continuity condition) via simulation [37], answering the fundamental question of whether this problem should be addressed as an inverse problem or as a semantic segmentation problem (finding the missing quotient). We have come to the conclusion that the most accurate results can be obtained by treating the phase unwrapping problem as an inverse problem, and then rounding the result to the closest integer addition of 2π, yielding an effective segmentation operation, as was previously proposed by Pritt, to account for inconsistency [38].…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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

Self Cite

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“…36,37,40,41,45 • Real data reprocessing (RDR): The absolute phase of real samples is obtained by traditional methods. 23,27,30,31,33 As shown in Fig. 3, the overall process of these deeplearning-involved phase unwrapping methods can be summarized as follows:…”

Section: (B)mentioning

confidence: 99%

“…• Deep-learning-performed regression method (dRG) estimates the absolute phase directly from the wrapped phase by the neural network. [22][23][24][25][26][27][28][29][30][31][32][33] The used dataset contains the paired wrapped phase as input and absolute phase as ground truth (GT), as shown in Fig. 2(a).…”

Section: Introductionmentioning

confidence: 99%

Deep learning spatial phase unwrapping: a comparative review

Wang

Kemao

et al. 2022

Adv. Photon. Nexus

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Phase unwrapping is an indispensable step for many optical imaging and metrology techniques. The rapid development of deep learning has brought ideas to phase unwrapping. In the past four years, various phase dataset generation methods and deep-learning-involved spatial phase unwrapping methods have emerged quickly. However, these methods were proposed and analyzed individually, using different strategies, neural networks, and datasets, and applied to different scenarios. It is thus necessary to do a detailed comparison of these deep-learning-involved methods and the traditional methods in the same context. We first divide the phase dataset generation methods into random matrix enlargement, Gauss matrix superposition, and Zernike polynomials superposition, and then divide the deep-learning-involved phase unwrapping methods into deep-learning-performed regression, deep-learning-performed wrap count, and deep-learningassisted denoising. For the phase dataset generation methods, the richness of the datasets and the generalization capabilities of the trained networks are compared in detail. In addition, the deep-learning-involved methods are analyzed and compared with the traditional methods in ideal, noisy, discontinuous, and aliasing cases. Finally, we give suggestions on the best methods for different situations and propose the potential development direction for the dataset generation method, neural network structure, generalization ability enhancement, and neural network training strategy for the deep-learning-involved spatial phase unwrapping methods.

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On the use of deep learning for phase recovery

Wang,

Song,

Wang

et al. 2024

Light Sci Appl

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Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource (https://github.com/kqwang/phase-recovery) for readers to learn more about PR.

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

Cited by 6 publications

References 23 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 learning spatial phase unwrapping: a comparative review

On the use of deep learning for phase recovery

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