Resources for phase recovery

Released from here

Here, we refer to “calculating the phase of a light field from
its amplitude/intensity measurements” as phase recovery (PR), which contains many techniques and algorithms, such as holography/interferometry,
transport of intensity equation (TIE), phase retrieval (optimization-based approaches), wavefront sensing, and deep-learning-based approaches.

Table of contents:

Contributing

You are welcome to join as a contributor, by adding or modifying relevant content via the “fork and pull request”.

Please use the following guidelines:

  • Edit the raw file with Markdown Syntax.
  • Avoid typos.
  • Don’t add what’s already there.
  • Uniform the format of new additions with that of the existing.
  • Note the order of new additions (chronological or alphabetical).

Back to Top

People and groups

(In alphabetical order according to surnames)
Quick search by “Ctrl + F” with the following keywords:
phase imaging, holography, interferometry, phase retrieval, (Fourier) ptychography, inverse problem,
transport of intensity equation, wavefront sensing, adaptive optics, phase unwrapping, fringe analysis,
coherent diffractive imaging, optical diffraction tomography, computational imaging, biomedical imaging

Asia

  • Arun Anand (Sardar Patel University)
    Keywords: holography and biomedical imaging etc.
  • Anand Asundi (d’Optron Pte Ltd)
    Keywords: phase imaging, holography, and transport of intensity equation (TIE) etc.
  • Liheng Bian (Beijing Institute of Technology)
    Keywords: phase retrieval etc.
  • Liangcai Cao (Tsinghua University)
    Keywords: holography etc.
  • Wen Chen (The Hong Kong Polytechnic University)
    Keywords: holography and single-pixel imaging etc.
  • Chau-Jern Cheng (National Taiwan Normal University)
    Keywords: holography etc.
  • Wonshik Choi (Korea university)
    Keywords: phase imaging and optical diffraction tomography etc.
  • Zachary J. Smith and Kaiqin Chu (The University of Science and Technology of China)
    Keywords: phase imaging and super-resolution imaging etc.
  • Qionghai Dai (Tsinghua university)
    Keywords: computational imaging and phase imaging etc.
  • Shai Dekel (Tel-Aviv University)
    Keywords: phase retrieval etc.
  • Jianglei Di (GuangDong University of Technology)
    Keywords: holography etc.
  • Peng Gao (Xidian University)
    Keywords: phase imaging and super-resolution imaging etc.
  • Ryoichi Horisaki (The University of Tokyo)
    Keywords: wavefront sensing, holography and computational imaging etc.
  • Wolfgang Heidrich (King Abdullah University of Science and Technology)
    Keywords: wavefront sensing, phase imaging and computational imaging etc.
  • Kedar Khare (Indian Institute of Technology Delhi)
    Keywords: holography, phase imaging, and computational imaging etc.
  • Edmund Y. Lam (The University of Hong Kong)
    Keywords: phase retrieval, holography and computational imaging etc.
  • Byoungho Lee (Seoul National University)
    Keywords: phase retrieval and holography etc.
  • Cheng Liu (Jiangnan University)
    Keywords: phase imaging etc.
  • Ne-Te Duane Loh (National University of Singapore)
    Keywords: phase retrieval and wavefront sensing etc.
  • Daniel P.K. Lun (The Hong Kong Polytechnic University)
    Keywords: phase retrieval and computational imaging etc.
  • Yuan Luo (National Taiwan University)
    Keywords: phase imaging etc.
  • Dalip Singh Mehta (Indian Institute of Technology Delhi)
    Keywords: phase imaging and holograohy etc.
  • Inkyu Moon (Daegu Gyeongbuk Institute of Science and Technology)
    Keywords: holograohy (in cell imaging and analysis) etc.
  • Takanori Nomura (Wakayama University)
    Keywords: holography etc.
  • An Pan (Chinese Academy of Sciences)
    Keywords: Fourier ptychography and phase imaging etc.
  • Jung-Hoon Park (Ulsan National Institute of Science and Technology)
    Keywords: phase imaging and biomedical imaging etc.
  • YongKeun Park (Korea Advanced Institute of Science and Technology)
    Keywords: phase imaging, optical diffraction tomography and biomedical imaging etc.
  • Kemao Qian (Nanyang Technological University)
    Keywords: phase unwrapping and fringe analysis etc.
  • Chenggen Quan (National University of Singapore)
    Keywords: phase retrieval and holography etc.
  • Liyong Ren (Shaanxi Normal University)
    Keywords: phase unwrapping etc.
  • Lu Rong (Beijing University of Technology)
    Keywords: phase retrieval, coherent diffractive imaging, and holography etc.
  • Joseph Rosen (Ben-Gurion University of the Negev)
    Keywords: holography etc.
  • Natan T. Shaked (Tel Aviv University)
    Keywords: interferometry, wavefront sensing, and biomedical imaging etc.
  • Tomoyoshi Shimobaba (Chiba University)
    Keywords: holography and phase retrieval etc.
  • Yoav Shechtman (Israel Institute of Technology)
    Keywords: phase retrieval etc.
  • Guohai Situ (University of Chinese Academy of Sciences)
    Keywords: phase imaging, holography, and computational imaging etc.
  • Yukio Takahashi (Tohoku University)
    Keywords: phase retrieval, holography, and coherent diffractive imaging etc.
  • Xiaodi Tan (Fujian Normal University)
    Keywords: holography etc.
  • Peter Wai Ming Tsang (City University of Hong Kong)
    Keywords: holography etc.
  • Yang Wang (The Hong Kong University of Science and Technology)
    Keywords: phase retrieval etc.
  • Dayong Wang (Beijing University of Technology)
    Keywords: holography etc.
  • Masahiro Yamaguchi (Tokyo Institute of Technology)
    Keywords: holography (display) etc.
  • Baoli Yao (University of Chinese Academy of Sciences)
    Keywords: holography and super-resolution imaging etc.
  • Masayuki Yokota (Shimane University)
    Keywords: holography etc.
  • Yingjie Yu (Shanghai University)
    Keywords: holography and computational imaging etc.
  • Caojin Yuan (Nanjing Normal University)
    Keywords: holography etc.
  • Fucai Zhang (Southern University of Science and Technology)
    Keywords: phase retrieval, wavefront sensing, and holography etc.
  • Shaohui Zhang (Beijing Institute of Technology)
    Keywords: phase retrieval and Fourier ptychography etc.
  • Yaping Zhang (Kunming University of Science and Technology)
    Keywords: holography etc.
  • Jianlin Zhao (Northwestern Polytechnical University)
    Keywords: holography and computational imaging etc.
  • Renjie Zhou (The Chinese University of Hong Kong)
    Keywords: phase imaging, optical diffraction tomography, and biomedical imaging etc.
  • Chao Zuo (Nanjing University of Science and Technology)
    Keywords: transport of intensity equation (TIE), phase imaging, and computational imaging etc.

Back to Top

Americas

  • George Barbastathis (Massachusetts Institute of Technology)
    Keywords: inverse problem, phase imaging, holography, and computational imaging etc.
  • Stephen Boppart (University of Illinois Urbana-Champaign)
    Keywords: wavefront sensing and biomedical imaging etc.
  • David J Brady (University of Arizona)
    Keywords: (compressive) holography and computational imaging etc.
  • Stanley H. Chan (Purdue University)
    Keywords: wavefront sensing etc.
  • Ni Chen (University of Arizona)
    Keywords: differentiable holography, coherent diffraction imaging, differentiable imaging, and phase imaging etc.
  • Mathew Cherukara (Argonne National Laboratory)
    Keywords: phase retrieval etc.
  • Ana Doblas (University of Memphis)
    Keywords: holography and phase imaging etc.
  • James R. Fienup (University of Rochester)
    Keywords: phase retrieval, coherent diffractive imaging, and wavefront sensing etc.
  • Jason W. Fleischer (Mississippi State University)
    Keywords: phase retrieval etc.
  • Joseph Goodman (Stanford University)
    Keywords: holography etc.
  • Peter de Groot (Zygo Corporation)
    Ketwords: interferometry etc.
  • Paul Hand (Northeastern University)
    Keywords: inverse problem and phase retrieval etc.
  • Babak Hassibi (California Institute of Technology)
    Keywords: phase retrieval etc.
  • Xiaojing Huang (Brookhaven National Laboratory)
    Keywords: coherent diffraction imaging and ptychography etc.
  • Roarke Horstmeyer (Duke University)
    Keywords: Fourier ptychography and single-photon detection etc.
  • Bahram Javidi (University of Connecticut)
    Keywords: holography etc.
  • Rongguang Liang (The University of Arizona)
    Keywords: phase imaging, phase unwrapping and biomedical imaging etc.
  • Christopher Metzler (The University of Maryland)
    Keywords: phase retrieval, inverse problem and computational imaging etc.
  • Jianwei (John) Miao (University of California, Los Angeles)
    Keywords: coherent diffractive imaging and atomic electron tomography etc.
  • David Nolte (Purdue University)
    Keywords: interferometry and holography etc.
  • Aydogan Ozcan (University of California, Los Angeles)
    Keywords: phase imaging, holography, and lensless imaging etc.
  • Ting-Chung Poon (Virginia Polytechnic Institute and State University)
    Keywords: holography etc.
  • Gabriel Popescu (University of Illinois at Urbana-Champaign)
    Keywords: phase imaging, optical diffraction tomography and biomedical imaging etc.
  • Mariano Rivera (Centro de Investigación en Matemáticas AC)
    Keywords: fringe analysis, phase retrieval, and phase unwrapping etc.
  • Ian Robinson (Brookhaven National Laboratory)
    Keywords: phase retrieval and coherent diffractive imaging etc.
  • Austin Roorda (University of California, Berkeley)
    Keywords: wavefront sensing and adaptive optics etc.
  • Sujay Sanghavi (University of Texas, Austin)
    Keywords: phase retrieval etc.
  • Philip Schniter (The Ohio State University)
    Keywords: inverse problem and phase retrieval etc.
  • Mahdi Soltanolkotabi (University of Southern California)
    Keywords: phase retrieval and computational imaging etc.
  • Adrian Stern (Ben-Gurion University of the Negev)
    Keywords: holography etc.
  • Ju Sun (University of Minnesota)
    Keywords: inverse problem and phase retrieval etc.
  • Enrique Tajahuerce (Universitat Jaume I)
    Keywords: holography and computational imaging etc.
  • Michael Teitell (University of California, Los Angeles)
    Keywords: phase imaging and biomedical imaging etc.
  • Lei Tian (Boston University)
    Keywords: Fourier ptychography, transport of intensity equation (TIE), and computational imaging etc.
  • Carlos Alejandro Trujillo (EAFIT University)
    Keywords: holography and phase imaging etc.
  • Ashok Veeraraghavan (Rice University)
    Keywords: wavefront sensing and lensless imaging etc.
  • Kent Wallace (Jet Propulsion Laboratory)
    Keywords: wavefront sensing, interferometry, and holography etc.
  • Laura Waller (University of California, Berkeley)
    Keywords: phase imaging, lensless imaging, and transport of intensity equation (TIE) etc.
  • Congli Wang (University of California, Berkeley)
    Keywords: wavefront sensing, adaptive optics, and digital holography etc.
  • Adam P. Wax (Duke University)
    Keywords: interferometry and biomedical imaging etc.
  • Gordon Wetzstein (Stanford University)
    Keywords: holography (display) and computational imaging etc.
  • Florian Willomitzer (University of Arizona)
    Keywords: synthetic wavelength holography and interferometry etc.
  • Changhuei Yang (California Institute of Technology)
    Keywords: Fourier ptychography, wavefront shaping, and non-line-of-sight imaging etc.
  • Zahid Yaqoob (Massachusetts Institute of Technology)
    Keywords: phase imaging etc.
  • Thomas A. Zangle (University of Utah)
    Keywords: phase imaging and biomedical imaging etc.
  • Guoan Zheng (University of Connecticut)
    Keywords: Fourier ptychography etc.
  • Yunhui Zhu (Virginia Polytechnic Institute and State University)
    Ketwords: transport of intensity equation (TIE) and phase imaging etc.

Back to Top

Europe

  • Martin Booth (University of Oxford)
    Keywords: wavefront sensing and adaptive optics etc.
  • Artur Carnicer (Universitat de Barcelona)
    Keywords: holography etc.
  • Radim Chmelik (Brno University of Technology)
    Keywords: phase imaging and holography etc.
  • Juergen Czarske (Dresden University of Technology)
    Keywords: holography and phase imaging etc.
  • Loïc Denis (Université Jean Monnet)
    Keywords: holography, phase retrieval, and wavefront sensing etc.
  • Karen Eguiazarian (Tampere University)
    Keywords: phase retrieval and computational imaging etc.
  • Tomas Ekeberg (Uppsala University)
    Keywords: phase retrieval and coherent diffractive imaging etc.
  • Pietro Ferraro (CNR-ISASI)
    Keywords: holography etc.
  • Hans-Werner Fink (University of Zurich)
    Keywords: electron holography etc.
  • Thierry Fusco (ONERA)
    Keywords: wavefront sensing and adaptive optics etc.
  • Guillermo Gallego (Technische Universität Berlin)
    Keywords: 3D surface reconstruction etc.
  • Sylvain Gigan (Sorbonne Université)
    Keywords: phase retrieval, imaging through scattering media, and computational imaging etc.
  • Manuel Guizar-Sicairos (EPFL)
    Keywords: phase retrieval, coherent diffractive imaging, and holography etc.
  • Stefan Harmeling (Technische Universität Dortmund)
    Keywords: phase retrieval etc.
  • Maxime Jacquot (Université Bourgogne Franche-Comté)
    Keywords: holography etc.
  • Vladimir Katkovnik (Tampere University of Technology)
    Keywords: phase retrieval and inverse problem etc.
  • Aykut Koc (Bilkent University)
    Keywords: phase retrieval, inverse problem, and computational imaging etc.
  • Christoph T. Koch (Humboldt University of Berlin)
    Keywords: phase retrieval and inverse problem etc.
  • Malgorzata Kujawinska (Warsaw University of Technology)
    Keywords: holography, phase imaging, and biomedical imaging etc.
  • Tatiana Latychevskaia (University of Zurich)
    Keywords: phase retrieval and holography etc.
  • Filipe Maia (Uppsala University)
    Keywords: phase retrieval and coherent diffractive imaging etc.
  • Andrew Maiden (The University of Sheffield)
    Keywords: coherent diffractive imaging, ptychography, phase imaging in the transmission electron Microscope (TEM), and inverse problems etc.
  • Pierre Marquet (Université Laval)
    Keywords: holography (holographic microscopy) etc.
  • Pasquale Memmolo (CNR-ISASI)
    Keywords: holography etc.
  • Thomas J. Naughton (National University of Ireland, Maynooth)
    Keywords: holography etc.
  • Figen S. Oktem (Middle East Technical University)
    Keywords: phase retrieval, inverse problem, and computational imaging etc.
  • Wolfgang Osten (University of Stuttgart)
    Keywords: interferometry and holography etc.
  • Nikolay V. Petrov (ITMO University)
    Keywords: phase retrieval and holography etc.
  • Demetri Psaltis (EPFL)
    Keywords: holography and optical diffraction tomography etc.
  • Pascal Picart (Le Mans University)
    Keywords: holography and phase imaging etc.
  • Benjamin Rappaz (EPFL)
    Keywords: holography (holographic microscopy) etc.
  • John Rodenburg (The University of Sheffield)
    Keywords: phase retrieval, coherent diffractive imaging, and ptychography etc.
  • Genaro Saavedra (Universitat de Valéncia)
    Keywords: holography, 3D imaging and 3D display etc.
  • Juergen Schnekenburger (Muenster University)
    Keywords: holography etc.
  • Latychevskaia Tatiana (University of Zurich)
    Keywords: holography, phase retrieval, and coherent diffractive imaging etc.
  • Michael Unser (EPFL)
    Keywords: phase retrieval, phase unwrapping, transport of intensity equation (TIE), and biomedical imaging etc.
  • Giovanni Volpe (Giovanni Volpe)
    Keywords: holography in biomedical and particle analysis etc.

Back to Top

Oceania

  • Andrew Lambert(University of New South Wales)
    Keywords: wavefront sensing and adaptive optics etc.
  • Rainer Leitgeb (Medical University of Vienna)
    Keywords: holography and biomedical imaging etc.
  • David Paganin (Monash University)
    Keywords: phase retrieval, coherent diffractive imaging, and phase imaging etc.
  • Konstantin Pavlov (University of Canterbury)
    Keywords: phase retrieval and coherent diffractive imaging etc.

Back to Top

Companies

(In alphabetical order)

  • d’Optron
    Keywords: digital holographic microscopy etc.

  • Holmarc Opto-Mechatronics
    Keywords: digital holographic microscopy, digital in-line holographic microscopy etc.

  • Imaging Optic
    Keywords: wavefront sensing etc

  • Lyncee Tec
    Keywords: digital holographic microscopy etc.

  • Nanolive
    Keywords: optical diffraction tomography etc.

  • Phase Holographic Imaging PHI AB
    Keywords: digital holographic microscopy etc.

  • Phase Focus
    Keywords: phase retrieval etc.

  • Phasics
    Keywords: wavefront sensing and quadriwave lateral shearing interferometry (QWLSI) etc.

  • Phi Optics
    Keywords: holographic microscopy and spatial light interference microscopy (SLIM) etc.

  • Telight
    Keywords: holographic microscopy etc.

  • Tomocube
    Keywords: optical diffraction tomography etc.

  • Trioptics
    Keywords: wavefront sensing and interferometry etc.

  • Zygo
    Keywords: interferometer and coherence scanning interferometric (CSI) profiler etc.

Back to Top

Workshops or courses (video or slides available)

(In chronological order)

Back to Top

Research papers

(In chronological order)

Conventional phase recovery

(Here, we only mention the classic pioneering papers)

Holography/Interferometry

  • D. Gabor

A New Microscopic Principle
Nature 161(4098), 777–778 (1948).

  • E. N. Leith and J. Upatnieks

Reconstructed Wavefronts and Communication Theory*
J. Opt. Soc. Am. 52(10), 1123 (1962).

  • I. Yamaguchi and T. Zhang

Phase-shifting digital holography
Opt. Lett. 22(16), 1268 (1997).

  • G. Popescu, T. Ikeda, R. R. Dasari, and M. S. Feld

Diffraction phase microscopy for quantifying cell structure and dynamics
Opt. Lett. 31(6), 775 (2006).

  • Z. Wang, L. Millet, M. Mir, H. Ding, S. Unarunotai, J. Rogers, M. U. Gillette, and G. Popescu

Spatial light interference microscopy (SLIM)
Opt. Express 19(2), 1016 (2011).

Transport of intensity equation

  • J. P. Guigay

Fourier transform analysis of Fresnel diffraction patterns and in-line holograms
Optik 49, 121–125 (1977).

  • D. Paganin and K. A. Nugent

Noninterferometric Phase Imaging with Partially Coherent Light
Phys. Rev. Lett. 80(12), 2586–2589 (1998).

  • M. R. Teague

Deterministic phase retrieval: a Green’s function solution
J. Opt. Soc. Am. 73(11), 1434 (1983).

Wavefront-sensing-based approaches

(Mainly refers to the approaches of obtaining the phase gradient first and then integrating to calculate the phase)

  • J. Hartmann

Bemerkungen uber den Bau und die Justirung von Spektrographen
Zeitschrift fuer Instrumentenkunde 20, 47–58 (1900).

  • R. V. Shack and B. C. Platt

Production and use of a lenticular Hartmann screen
J. Opt. Soc. Am. 61, 656 (1971).

  • J. S. Hartman, R. L. Gordon, and D. L. Lessor

Development Of Nomarski Microscopy For Quantitative Determination Of Surface Topography(A)
in G. W. Hopkins, ed. (1979), pp. 223–230. (Conference)

  • P. Bon, G. Maucort, B. Wattellier, and S. Monneret

Quadriwave lateral shearing interferometry for quantitative phase microscopy of living cells
Opt. Express 17(15), 13080 (2009).

Back to Top

Optimization-based approaches

Alternating projection

  • R. W. Gerchberg

A practical algorithm for determination of phase from image and diffraction plane pictures
Optik 35, 237–246 (1972).

  • J. R. Fienup

Reconstruction of an object from the modulus of its Fourier transform
Opt. Lett. 3(1), 27 (1978).

  • J. R. Fienup

Phase retrieval algorithms: a comparison
Appl. Opt. 21(15), 2758 (1982).

Axial multi-intensity alternating projection (Multi-height phase retrieval)

  • L. J. Allen and M. P. Oxley

Phase retrieval from series of images obtained by defocus variation
Optics Communications 199(1–4), 65–75 (2001).

  • G. Pedrini, W. Osten, and Y. Zhang

Wave-front reconstruction from a sequence of interferograms recorded at different planes
Opt. Lett. 30(8), 833 (2005).

  • A. Greenbaum and A. Ozcan

Maskless imaging of dense samples using pixel super-resolution based multi-height lensfree on-chip microscopy
Opt. Express 20(3), 3129 (2012).

Radial multi-intensity alternating projection (Real-space ptychography)

  • H. M. L. Faulkner and J. M. Rodenburg

Movable Aperture Lensless Transmission Microscopy: A Novel Phase Retrieval Algorithm
Phys. Rev. Lett. 93(2), 023903 (2004).

  • J. M. Rodenburg and H. M. L. Faulkner

A phase retrieval algorithm for shifting illumination
Appl. Phys. Lett. 85(20), 4795–4797 (2004).

Angular multi-intensity alternating projection (Fourier ptychography)

  • G. Zheng, R. Horstmeyer, and C. Yang

Wide-field, high-resolution Fourier ptychographic microscopy
Nature Photon 7(9), 739–745 (2013).

  • X. Ou, R. Horstmeyer, C. Yang, and G. Zheng

Quantitative phase imaging via Fourier ptychographic microscopy
Opt. Lett. 38(22), 4845 (2013).

Non-convex optimization

  • E. J. Candes, X. Li, and M. Soltanolkotabi

Phase Retrieval via Wirtinger Flow: Theory and Algorithms
IEEE Trans. Inform. Theory 61(4), 1985–2007 (2015).

  • G. Wang, G. B. Giannakis, and Y. C. Eldar

Solving Systems of Random Quadratic Equations via Truncated Amplitude Flow
IEEE Trans. Inform. Theory 64(2), 773–794 (2018).

Convex optimization

  • E. J. Candès, T. Strohmer, and V. Voroninski

PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming
Comm. Pure Appl. Math. 66(8), 1241–1274 (2013).

Back to Top

Deep-learning(DL)-based phase recovery

DL-pre-processing for phase recovery

Pixel super-resolution

  • Z. Luo, A. Yurt, R. Stahl, A. Lambrechts, V. Reumers, D. Braeken, and L. Lagae

Pixel super-resolution for lens-free holographic microscopy using deep learning neural networks
Opt. Express 27(10), 13581 (2019).

  • H. Byeon, T. Go, and S. J. Lee

Deep learning-based digital in-line holographic microscopy for high resolution with extended field of view
Optics & Laser Technology 113, 77–86 (2019).

  • Z. Ren, H. K.-H. So, and E. Y. Lam

Fringe Pattern Improvement and Super-Resolution Using Deep Learning in Digital Holography
IEEE Trans. Ind. Inf. 15(11), 6179–6186 (2019).

  • L. Xin, X. Liu, Z. Yang, X. Zhang, Z. Gao, and Z. Liu

Three-dimensional reconstruction of super-resolved white-light interferograms based on deep learning
Optics and Lasers in Engineering 145, 106663 (2021).

Noise reduction

  • K. Yan, Y. Yu, C. Huang, L. Sui, K. Qian, and A. Asundi

Fringe pattern denoising based on deep learning
Optics Communications 437, 148–152 (2019).

  • F. Hao, C. Tang, M. Xu, and Z. Lei

Batch denoising of ESPI fringe patterns based on convolutional neural network
Appl. Opt. 58(13), 3338 (2019).

  • W.-J. Zhou, S. Zou, D.-K. He, J.-L. Hu, H. Zhang, Y.-J. Yu, and T.-C. Poon

Speckle noise reduction in digital holograms based on Spectral Convolutional Neural Networks (SCNN)
in Holography, Diffractive Optics, and Applications IX, C. Zhou, Y. Sheng, and L. Cao, eds. (SPIE, 2019), p. 6.

  • B. Lin, S. Fu, C. Zhang, F. Wang, and Y. Li

Optical fringe patterns filtering based on multi-stage convolution neural network
Optics and Lasers in Engineering 126, 105853 (2020).

  • W.-J. Zhou, S. Liu, H. Zhang, Y. Yu, and T.-C. Poon

A Deep Learning Approach for Digital Hologram Speckle Noise Reduction
in Imaging and Applied Optics Congress (Optica Publishing Group, 2020), p. HTu5B.5.

  • A. Reyes-Figueroa, V. H. Flores, and M. Rivera

Deep neural network for fringe pattern filtering and normalization
Appl. Opt. 60(7), 2022 (2021).

  • J. Gurrola-Ramos, O. Dalmau, and T. Alarcón

U-Net based neural network for fringe pattern denoising
Optics and Lasers in Engineering 149, 106829 (2022).

Hologram generation

(for phase-shifting)

  • Q. Zhang, S. Lu, J. Li, W. Li, D. Li, X. Lu, L. Zhong, and J. Tian

Deep Phase Shifter for Quantitative Phase Imaging
Preprint at arXiv (2020).

  • Q. Zhang, S. Lu, J. Li, D. Li, X. Lu, L. Zhong, and J. Tian

Phase-shifting interferometry from single frame in-line interferogram using deep learning phase-shifting technology
Optics Communications 498, 127226 (2021).

  • K. Yan, A. Khan, A. Asundi, Y. Zhang, and Y. Yu

Virtual temporal phase-shifting phase extraction using generative adversarial networks
Appl. Opt. 61(10), 2525 (2022).

  • Y. Zhao, K. Hu, and F. Liu

One-shot phase retrieval method for interferometry using a multi-stage phase-shifting network
IEEE Photon. Technol. Lett. 35, 577–580 (2022).

  • T. Huang, Q. Zhang, J. Li, X. Lu, J. Di, L. Zhong, and Y. Qin

Single-shot Fresnel incoherent correlation holography via deep learning based phase-shifting technology
Opt. Express 31(8), 12349 (2023).

  • B. Wu, Q. Zhang, T. Liu, Q. Ma, and J. Li

RSAGAN: Rapid self-attention generative adversarial nets for single-shot phase-shifting interferometry
Optics and Lasers in Engineering 168, 107672 (2023).

(to different defocus distances)

  • J. Gurrola-Ramos,

Diffraction-Net: a robust single-shot holography for multi-distance lensless imaging
Opt. Express 30(23), 41724 (2022).

(for multi-wavelength holography)

  • J. Li, Q. Zhang, L. Zhong, J. Tian, G. Pedrini, and X. Lu

Quantitative phase imaging in dual-wavelength interferometry using a single wavelength illumination and deep learning
Opt. Express 28(19), 28140 (2020).

  • J. Li, Q. Zhang, L. Zhong, and X. Lu

Hybrid-net: a two-to-one deep learning framework for three-wavelength phase-shifting interferometry
Opt. Express 29(21), 34656 (2021).

  • X. Xu, M. Xie, Y. Ji, and Y. Wang

Dual-wavelength interferogram decoupling method for three-frame generalized dual-wavelength phase-shifting interferometry based on deep learning
J. Opt. Soc. Am. A 38(3), 321 (2021).

Autofocusing

(by classification)

  • T. Pitkäaho, A. Manninen, and T. J. Naughton

Performance of Autofocus Capability of Deep Convolutional Neural Networks in Digital Holographic Microscopy
in Digital Holography and Three-Dimensional Imaging (OSA, 2017), p. W2A.5.

  • T. Pitkäaho, A. Manninen, and T. J. Naughton

Focus classification in digital holographic microscopy using deep convolutional neural networks
in E. Beaurepaire, F. S. Pavone, and P. T. C. So, eds. (2017), p. 104140K.

  • Z. Ren, Z. Xu, and E. Y. M. Lam

Autofocusing in digital holography using deep learning
in Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXV (SPIE, 2018), p. 56.

  • K. Son, W. Jeong, W. Jeon, and H. Yang

Autofocusing algorithm for a digital holographic imaging system using convolutional neural networks
Jpn. J. Appl. Phys. 57(9S1), 09SB02 (2018).

  • R. Couturier, M. Salomon, E. A. Zeid, and C. A. Jaoude

Using Deep Learning for Object Distance Prediction in Digital Holography
in 2021 International Conference on Computer, Control and Robotics (ICCCR) (IEEE, 2021), pp. 231–235.

(by regression)

  • Z. Ren, Z. Xu, and E. Y. Lam

Learning-based nonparametric autofocusing for digital holography
Optica 5(4), 337 (2018).

  • J.-S. Lee

Autofocusing using deep learning in off-axis digital holography
in Imaging and Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&C, MATH, PcAOP) (OSA, 2018), p. DTh1C.4.

  • T. Shimobaba, T. Kakue, and T. Ito

Convolutional Neural Network-Based Regression for Depth Prediction in Digital Holography
in 2018 IEEE 27th International Symposium on Industrial Electronics (ISIE) (IEEE, 2018), pp. 1323–1326.

  • T. Pitkäaho, A. Manninen, and T. J. Naughton

Focus prediction in digital holographic microscopy using deep convolutional neural networks
Appl. Opt. 58(5), A202 (2019).

  • K. Jaferzadeh, S.-H. Hwang, I. Moon, and B. Javidi

No-search focus prediction at the single cell level in digital holographic imaging with deep convolutional neural network
Biomed. Opt. Express 10(8), 4276 (2019).

  • I. Moon and K. Jaferzadeh

Automated digital holographic image reconstruction with deep convolutional neural networks
in Three-Dimensional Imaging, Visualization, and Display 2020, (SPIE, 2020), p. 10.

  • S. Cuenat and R. Couturier

Convolutional Neural Network (CNN) vs Vision Transformer (ViT) for Digital Holography
in 2022 2nd International Conference on Computer, Control and Robotics (ICCCR) (IEEE, 2022), pp. 235–240.

  • S. Cuenat, L. Andréoli, A. N. André, P. Sandoz, G. J. Laurent, R. Couturier, and M. Jacquot

Fast autofocusing using tiny transformer networks for digital holographic microscopy
Opt. Express 30(14), 24730 (2022).

Back to Top

DL-in-processing for phase recovery

Dataset-driven (DD) network-only strategy

  • A. Sinha, J. Lee, S. Li, and G. Barbastathis

Lensless computational imaging through deep learning
Optica 4(9), 1117 (2017).

  • H. Wang, M. Lyu, and G. Situ

eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction
Opt. Express 26(18), 22603 (2018).

  • T. Nguyen, Y. Xue, Y. Li, L. Tian, and G. Nehmetallah

Deep learning approach for Fourier ptychography microscopy
Opt. Express 26(20), 26470 (2018).

  • S. Li and G. Barbastathis

Spectral pre-modulation of training examples enhances the spatial resolution of the phase extraction neural network (PhENN)
Opt. Express 26(22), 29340 (2018).

  • M. J. Cherukara, Y. S. G. Nashed, and R. J. Harder

Real-time coherent diffraction inversion using deep generative networks
Sci Rep 8(1), 16520 (2018).

  • A. Goy, K. Arthur, S. Li, and G. Barbastathis

Low Photon Count Phase Retrieval Using Deep Learning
Phys. Rev. Lett. 121(24), 243902 (2018).

  • Y. F. Cheng, M. Strachan, Z. Weiss, M. Deb, D. Carone, and V. Ganapati

Illumination pattern design with deep learning for single-shot Fourier ptychographic microscopy
Opt. Express 27(2), 644 (2019).

  • Z. Ren, Z. Xu, and E. Y. Lam

End-to-end deep learning framework for digital holographic reconstruction
Adv. Photon. 1(01), 1 (2019).

  • X. Li, H. Qi, S. Jiang, P. Song, G. Zheng, and Y. Zhang

Quantitative phase imaging via a cGAN network with dual intensity images captured under centrosymmetric illumination
Opt. Lett. 44(11), 2879 (2019).

  • K. Wang, J. Dou, Q. Kemao, J. Di, and J. Zhao

Y-Net: a one-to-two deep learning framework for digital holographic reconstruction
Opt. Lett. 44(19), 4765 (2019).

  • Y. Nishizaki, R. Horisaki, K. Kitaguchi, M. Saito, and J. Tanida

Analysis of non-iterative phase retrieval based on machine learning
Opt Rev 27(1), 136–141 (2020).

  • T. Zeng, H. K.-H. So, and E. Y. Lam

RedCap: residual encoder-decoder capsule network for holographic image reconstruction
Opt. Express 28(4), 4876 (2020).

  • D. Yin, Z. Gu, Y. Zhang, F. Gu, S. Nie, J. Ma, and C. Yuan

Digital Holographic Reconstruction Based on Deep Learning Framework With Unpaired Data
IEEE Photonics J. 12(2), 1–12 (2020).

  • L. Hu, S. Hu, W. Gong, and K. Si

Deep learning assisted Shack–Hartmann wavefront sensor for direct wavefront detection
Opt. Lett. 45(13), 3741 (2020).

  • K. Wang, Q. Kemao, J. Di, and J. Zha

Y4-Net: a deep learning solution to one-shot dual-wavelength digital holographic reconstruction
Opt. Lett. 45(15), 4220 (2020).

  • M. Deng, S. Li, Z. Zhang, I. Kang, N. X. Fang, and G. Barbastathis

On the interplay between physical and content priors in deep learning for computational imaging
Opt. Express 28(16), 24152 (2020).

  • K. Wang, J. Di, Y. Li, Z. Ren, Q. Kemao, and J. Zhao

Transport of intensity equation from a single intensity image via deep learning
Opt. Lasers Eng. 134, 106233 (2020).

  • L. Wu, P. Juhas, S. Yoo, and I. Robinson

Complex imaging of phase domains by deep neural networks
IUCrJ 8(1), 12–21 (2021).

  • L. Huang, T. Liu, X. Yang, Y. Luo, Y. Rivenson, and A. Ozcan

Holographic Image Reconstruction with Phase Recovery and Autofocusing Using Recurrent Neural Networks
ACS Photonics 8(6), 1763–1774 (2021).

  • T. Uelwer, T. Hoffmann, and S. Harmeling

Non-iterative Phase Retrieval with Cascaded Neural Networks
in Artificial Neural Networks and Machine Learning – ICANN 2021 (Springer International Publishing, 2021), 12892, pp. 295–306.

  • R. Castaneda, C. Trujillo, and A. Doblas

Video-Rate Quantitative Phase Imaging Using a Digital Holographic Microscope and a Generative Adversarial Network
Sensors 21(23), 8021 (2021).

  • D. Pirone, D. Sirico, L. Miccio, V. Bianco, M. Mugnano, P. Ferraro, and P. Memmolo

Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning
Lab Chip 22(4), 793–804 (2022).

  • W. Luo, Y. Zhang, X. Shu, M. Niu, and R. Zhou

Learning end-to-end phase retrieval using only one interferogram with mixed-context network
in Quantitative Phase Imaging VIII, G. Popescu, Y. Park, and Y. Liu, eds. (SPIE, 2022), p. 26.

  • K. Jaferzadeh and T. Fevens

HoloPhaseNet: fully automated deep-learning-based hologram reconstruction using a conditional generative adversarial model
Biomed. Opt. Express 13(7), 4032 (2022).

  • H. Ding, F. Li, X. Chen, J. Ma, S. Nie, R. Ye, and C. Yuan

ContransGAN: Convolutional Neural Network Coupling Global Swin-Transformer Network for High-Resolution Quantitative Phase Imaging with Unpaired Data
Cells 11(15), 2394 (2022).

  • H. Chen, L. Huang, T. Liu, and A. Ozcan

Fourier Imager Network (FIN): A deep neural network for hologram reconstruction with superior external generalization
Light Sci Appl 11(1), 254 (2022).

  • Q. Ye, L.-W. Wang, and D. P. K. Lun

SiSPRNet: end-to-end learning for single-shot phase retrieval
Opt. Express 30(18), 31937 (2022).

  • X. Shu, M. Niu, Y. Zhang, and R. Zhou

NAS-PRNet: Neural Architecture Search generated Phase Retrieval Net for Off-axis Quantitative Phase Imaging
Preprint at arXiv (2022).

  • C. Lee, G. Song, H. Kim, J. C. Ye, and M. Jang

Deep learning based on parameterized physical forward model for adaptive holographic imaging with unpaired data
Nat Mach Intell 5, 35–45 (2023).

  • H. Chen, L. Huang, T. Liu, and A. Ozcan

eFIN: Enhanced Fourier Imager Network for Generalizable Autofocusing and Pixel Super-Resolution in Holographic Imaging
PhysIEEE J. Select. Topics Quantum Electron. 29(4: Biophotonics), 1–10 (2023).

(Distortion intensity of target objects → Network → Wavefront phase or its Zernike coefficient)
(Applications in Adaptive Optics)

  • Y. Nishizaki, M. Valdivia, R. Horisaki, K. Kitaguchi, M. Saito, J. Tanida, and E. Vera

Deep learning wavefront sensing
Opt. Express 27(1), 240 (2019).

  • H. Ma, H. Liu, Y. Qiao, X. Li, and W. Zhang

Numerical study of adaptive optics compensation based on Convolutional Neural Networks
Optics Communications 433, 283–289 (2019).

  • Q. Tian, C. Lu, B. Liu, L. Zhu, X. Pan, Q. Zhang, L. Yang, F. Tian, and X. Xin

DNN-based aberration correction in a wavefront sensorless adaptive optics system
Opt. Express 27(8), 10765 (2019).

  • J. Liu, P. Wang, X. Zhang, Y. He, X. Zhou, H. Ye, Y. Li, S. Xu, S. Chen, and D. Fan

Deep learning based atmospheric turbulence compensation for orbital angular momentum beam distortion and communication
Opt. Express 27(12), 16671 (2019).

  • Y. Zhang, C. Wu, Y. Song, K. Si, Y. Zheng, L. Hu, J. Chen, L. Tang, and W. Gong

Machine learning based adaptive optics for doughnut-shaped beam
Opt. Express 27(12), 16871 (2019).

  • H. Guo, Y. Xu, Q. Li, S. Du, D. He, Q. Wang, and Y. Huang

Improved Machine Learning Approach for Wavefront Sensing
Sensors 19(16), 3533 (2019).

  • Y. Xu, D. He, Q. Wang, H. Guo, Q. Li, Z. Xie, and Y. Huang

An Improved Method of Measuring Wavefront Aberration Based on Image with Machine Learning in Free Space Optical Communication
Sensors 19(17), 3665 (2019).

  • Q. Xin, G. Ju, C. Zhang, and S. Xu

Object-independent image-based wavefront sensing approach using phase diversity images and deep learning
Opt. Express 27(18), 26102 (2019).

  • T. Andersen, M. Owner-Petersen, and A. Enmark

Neural networks for image-based wavefront sensing for astronomy
Opt. Lett. 44(18), 4618 (2019).

  • B. P. Cumming, M. Gu, and M. Gu

Direct determination of aberration functions in microscopy by an artificial neural network
Opt. Express, OE 28(10), 14511–14521 (2020).

  • I. Vishniakou and J. D. Seelig

Wavefront correction for adaptive optics with reflected light and deep neural networks
Opt. Express 28(10), 15459 (2020).

  • Y. Wu, Y. Guo, H. Bao, and C. Rao

Sub-Millisecond Phase Retrieval for Phase-Diversity Wavefront Sensor
Sensors 20(17), 4877 (2020).

  • X. Wang, T. Wu, C. Dong, H. Zhu, Z. Zhu, and S. Zhao

Integrating deep learning to achieve phase compensation for free-space orbital-angular-momentum-encoded quantum key distribution under atmospheric turbulence
Photon. Res. 9(2), B9 (2021).

  • E. Vera, F. Guzmán, and C. Weinberger

Boosting the deep learning wavefront sensor for real-time applications
Appl. Opt. 60(10), B119 (2021).

  • Y. He, Z. Liu, Y. Ning, J. Li, X. Xu, and Z. Jiang

Deep learning wavefront sensing method for Shack-Hartmann sensors with sparse sub-apertures
Opt. Express 29(11), 17669 (2021).

  • S. Hu, L. Hu, W. Gong, Z. Li, and K. Si

Deep learning based wavefront sensor for complex wavefront detection in adaptive optical microscopes
Front Inform Technol Electron Eng 22(10), 1277–1288 (2021).

  • K. Wang, M. Zhang, J. Tang, L. Wang, L. Hu, X. Wu, W. Li, J. Di, G. Liu, and J. Zhao

Deep learning wavefront sensing and aberration correction in atmospheric turbulence
PhotoniX 2(1), 8 (2021).

Physics-driven (PD) network-only strategy

(with untrained/initialized networks)

  • L. Boominathan, M. Maniparambil, H. Gupta, R. Baburajan, and K. Mitra

Phase retrieval for Fourier Ptychography under varying amount of measurements
Preprint at arXiv (2018).

  • F. Wang, Y. Bian, H. Wang, M. Lyu, G. Pedrini, W. Osten, G. Barbastathis, and G. Situ

Phase imaging with an untrained neural network
Light Sci Appl 9(1), 77 (2020).

  • D. Yang, J. Zhang, Y. Tao, W. Lv, S. Lu, H. Chen, W. Xu, and Y. Shi

Dynamic coherent diffractive imaging with a physics-driven untrained learning method
Opt. Express 29(20), 31426 (2021).

  • C. Bai, T. Peng, J. Min, R. Li, Y. Zhou, and B. Yao

Dual-wavelength in-line digital holography with untrained deep neural networks
Photon. Res. 9(12), 2501 (2021).

  • X. Zhang, F. Wang, and G. Situ

BlindNet: an untrained learning approach toward computational imaging with model uncertaint
J. Phys. D: Appl. Phys. 55(3), 034001 (2022).

  • D. Yang, J. Zhang, Y. Tao, W. Lv, Y. Zhu, T. Ruan, H. Chen, X. Jin, Z. Wang, J. Qiu, and Y. Shi

Coherent modulation imaging using a physics-driven neural network
Opt. Express 30(20), 35647 (2022).

  • A. S. Galande, V. Thapa, H. P. R. Gurram, and R. John

Untrained deep network powered with explicit denoiser for phase recovery in inline holography
Appl. Phys. Lett. 122(13), 133701 (2023).

(with trained networks)

  • L. Wu, S. Yoo, A. F. Suzana, T. A. Assefa, J. C. Diao, R. J. Harder, W. Cha and I. K. Robinson

Three-dimensional coherent X-ray diffraction imaging via deep convolutional neural networks
npj Comput Mater 7(1), 124 (2022).

  • Y. Yao, H. Chan, S. Sankaranarayanan, P. Balaprakash, R. J. Harder, and M. J. Cherukara

AutoPhaseNN: unsupervised physics-aware deep learning of 3D nanoscale Bragg coherent diffraction imagin
npj Comput Mater 8(1), 124 (2022).

  • R. Li, G. Pedrini, Z. Huang, S. Reichelt, and L. Cao

Physics-enhanced neural network for phase retrieval from two diffraction patterns
Opt. Express 30(18), 32680 (2022).

  • L. Huang, H. Chen, T. Liu, and A. Ozcan

GedankenNet: Self-supervised learning of hologram reconstruction using physics consistency
Nat Mach Intell 5, 895–907 (2023).

  • L. Bouchama, B. Dorizzi, J. Klossa, and Y. Gottesman

A physics-inspired deep learning framework for an efficient FPM reconstruction under low overlap conditions
Preprint at preprints.opticaopen.org (2023).

  • O. Hoidn, A. Mishra, and A. Mehta

Physics constrained unsupervised deep learning for rapid, high resolution scanning coherent diffraction reconstruction
Sci Rep 13, 22789 (2023).

Physics-connect-network (PcN) strategy

  • Y. Rivenson, Y. Zhang, H. Günaydın, D. Teng, and A. Ozcan

Phase recovery and holographic image reconstruction using deep learning in neural networks
Light Sci Appl 7(2), 17141 (2018).

  • Y. Wu, Y. Rivenson, Y. Zhang, Z. Wei, H. Günaydin, X. Lin, and A. Ozcan

Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery
Optica 5(6), 704 (2018).

Low Photon Count Phase Retrieval Using Deep Learning

  • J. Zhang, T. Xu, Z. Shen, Y. Qiao, and Y. Zhang

Fourier ptychographic microscopy reconstruction with multiscale deep residual network
Opt. Express 27(6), 8612 (2019).

  • M. Deng, A. Goy, S. Li, K. Arthur, and G. Barbastathis

Probing shallower: perceptual loss trained Phase Extraction Neural Network (PLT-PhENN) for artifact-free reconstruction at low photon budget
Opt. Express 28(2), 2511 (2020).

  • M. Deng, S. Li, A. Goy, I. Kang, and G. Barbastathis

Learning to synthesize: robust phase retrieval at low photon counts
Light Sci Appl 9(1), 36 (2020).

  • I. Kang, F. Zhang, and G. Barbastathis

Phase extraction neural network (PhENN) with coherent modulation imaging (CMI) for phase retrieval at low photon counts
Opt. Express 28(15), 21578 (2020).

  • I. Moon, K. Jaferzadeh, Y. Kim, and B. Javidi

Noise-free quantitative phase imaging in Gabor holography with conditional generative adversarial network
Opt. Express 28(18), 26284 (2020).

Holographic Image Reconstruction with Phase Recovery and Autofocusing Using Recurrent Neural Networks

Network-in-physics (NiP) strategy

(trained networks as denoiser regularization)

  • C. A. Metzler, P. Schniter, A. Veeraraghavan, and R. G. Baraniuk

prDeep: Robust Phase Retrieval with a Flexible Deep Network
Preprint at arXiv (2018).

  • Ç. Işıl, F. S. Oktem, and A. Koç

Deep iterative reconstruction for phase retrieval
Appl. Opt. 58(20), 5422 (2019).

  • Z. Wu, Y. Sun, J. Liu, and U. Kamilov

Online Regularization by Denoising with Applications to Phase Retrieval
in 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) (IEEE, 2019), pp. 3887–3895.

  • C. Bai, M. Zhou, J. Min, S. Dang, X. Yu, P. Zhang, T. Peng, and B. Yao

Robust contrast-transfer-function phase retrieval via flexible deep learning networks
Opt. Lett. 44(21), 5141 (2019).

  • Y. Wang, X. Sun, and J. W. Fleischer

When deep denoising meets iterative phase retrieval
Preprint at arXiv (2020).

  • X. Chang, L. Bian, and J. Zhang

Large-scale phase retrieval
eLight 1(1), 4 (2021).

  • S. Kumar

Phase retrieval with physics informed zero-shot network
Opt. Lett. 46(23), 5942 (2021).

(untrained networks as structural-prior regularization)

  • G. Jagatap and C. Hegde

Phase Retrieval using Untrained Neural Network Priors
in NeurIPS 2019 Workshop on Solving Inverse Problems with Deep Networks (2019).

  • G. Jagatap and C. Hegde

Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors
in Advances in Neural Information Processing Systems 32 (2019).

  • K. C. Zhou and R. Horstmeyer

Diffraction tomography with a deep image prior
Opt. Express 28(9), 12872 (2020).

  • F. Shamshad, A. Hanif, and A. Ahmed

Subsampled Fourier Ptychography using Pretrained Invertible and Untrained Network Priors
Preprint at arXiv (2020).

  • E. Bostan, R. Heckel, M. Chen, M. Kellman, and L. Waller

Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network
Optica 7(6), 559 (2020).

  • H. Lawrence, D. A. Barmherzig, H. Li, M. Eickenberg, and M. Gabrié

Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging
Preprint at arXiv (2021).

  • F. Niknam, H. Qazvini, and H. Latifi

Holographic optical field recovery using a regularized untrained deep decoder network
Sci Rep 11(1), 10903 (2021).

  • L. Ma, H. Wang, N. Leng, and Z. Yuan

ADMM based Fourier phase retrieval with untrained generative prior
Preprint at arXiv (2022)

  • Q. Chen, D. Huang, and R. Chen

Fourier ptychographic microscopy with untrained deep neural network priors
Opt. Express 30(22), 39597 (2022).

(trained networks as generative-prior regularization)

  • P. Hand, O. Leong, and V. Voroninski

Phase Retrieval Under a Generative Prior
in Advances in Neural Information Processing Systems 31 (2018).

  • F. Shamshad and A. Ahmed

Robust Compressive Phase Retrieval via Deep Generative Priors
Preprint at arXiv (2018).

  • R. Hyder, V. Shah, C. Hegde, and M. S. Asif

Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval
in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2019), pp. 7705–7709.

  • F. Shamshad, F. Abbas, and A. Ahmed

Deep Ptych: Subsampled Fourier Ptychography Using Generative Priors
in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2019), pp. 7720–7724.

  • F. Shamshad and A. Ahmed

Compressed Sensing-Based Robust Phase Retrieval via Deep Generative Priors
IEEE Sensors J. 21(2), 2286–2298 (2021).

  • T. Uelwer, S. Konietzny, and S. Harmeling

Optimizing Intermediate Representations of Generative Models for Phase Retrieval
Preprint at arXiv (2022).

Physics-in-network (PiN) strategy

  • C.-J. Wang, C.-K. Wen, S.-H. Tsai, and S. Jin

Phase Retrieval With Learning Unfolded Expectation Consistent Signal Recovery Algorithm
IEEE Signal Process. Lett. 27, 780–784 (2020).

  • N. Naimipour, S. Khobahi, and M. Soltanalian

UPR: A Model-Driven Architecture for Deep Phase Retrieval
in 2020 54th Asilomar Conference on Signals, Systems, and Computers (IEEE, 2020), pp. 205–209.

  • N. Naimipour, S. Khobahi, and M. Soltanalian

Unfolded Algorithms for Deep Phase Retrieval
Preprint at arXiv (2020).

  • F. Zhang, X. Liu, C. Guo, S. Lin, J. Jiang, and X. Ji

Physics-based Iterative Projection Complex Neural Network for Phase Retrieval in Lensless Microscopy Imaging
in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, 2021), pp. 10518–10526.

  • B. Shi, Y. Gao, K. Jiang, and Q. Lian.

Convolutional Sparse Coding with Weighted L1 Norm for Phase Retrieval: Algorithm and Its Deep Unfolded Network
in 2022 IEEE International Conference on Image Processing (ICIP) (IEEE, 2022), pp. 1746–1750.

  • X. Wu, Z. Wu, S. C. Shanmugavel, H. Z. Yu, and Y. Zhu

Physics-informed neural network for phase imaging based on transport of intensity equation
Opt. Express 30(24), 43398 (2022).

  • Y. Yang, Q. Lian, X. Zhang, D. Zhang, and H. Zhang

HIONet: Deep priors based deep unfolded network for phase retrieval
Digital Signal Processing 132, 103797 (2022).

Back to Top

DL-post-processing for phase recovery

Noise reduction

  • W. Jeon, W. Jeong, K. Son, and H. Yang

Speckle noise reduction for digital holographic images using multi-scale convolutional neural networks
Opt. Lett. 43(17), 4240 (2018).

  • G. Choi, D. Ryu, Y. Jo, Y. S. Kim, W. Park, H. Min, and Y. Park

Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography
Opt. Express 27(4), 4927 (2019).

  • J. Zhang, X. Tian, J. Shao, H. Luo, and R. Liang

Phase unwrapping in optical metrology via denoised and convolutional segmentation networks
Opt. Express 27(10), 14903 (2019).

  • K. Yan, Y. Yu, T. Sun, A. Asundi, and Q. Kemao

Wrapped phase denoising using convolutional neural networks
Optics and Lasers in Engineering 128, 105999 (2020).

  • S. Montresor, M. Tahon, A. Laurent, and P. Picart

Computational de-noising based on deep learning for phase data in digital holographic interferometry
APL Photonics 5(3), 030802 (2020).

  • K. Yan, L. Chang, M. Andrianakis, V. Tornari, and Y. Yu

Deep Learning-Based Wrapped Phase Denoising Method for Application in Digital Holographic Speckle Pattern Interferometry
Applied Sciences 10(11), 4044 (2020).

  • M. Tahon, S. Montresor, and P. Picart

Towards Reduced CNNs for De-Noising Phase Images Corrupted with Speckle Noise
Photonics 8(7), 255 (2021).

  • Q. Fang, H. Xia, Q. Song, M. Zhang, R. Guo, S. Montresor, and P. Picart

Speckle denoising based on deep learning via a conditional generative adversarial network in digital holographic interferometry
Opt. Express 30(12), 20666 (2022).

  • M. Tahon, S. Montrésor, and P. Picart

Deep Learning Network for Speckle De-Noising in Severe Conditions
J. Imaging 8(6), 165 (2022).

  • G. Murdaca, A. Rucci, and C. Prati

Deep Learning for InSAR Phase Filtering: An Optimized Framework for Phase Unwrapping
Remote Sensing 14(19), 4956 (2022).

Resolution enhancement

  • T. Liu, K. de Haan, Y. Rivenson, Z. Wei, X. Zeng, Y. Zhang, and A. Ozcan

Deep learning-based super-resolution in coherent imaging systems
Sci Rep 9(1), 3926 (2019).

  • J. Lim, A. B. Ayoub, and D. Psaltis

Three-dimensional tomography of red blood cells using deep learning
Adv. Photon. 2(02), 1 (2020).

  • A. Butola, S. R. Kanade, S. Bhatt, V. K. Dubey, A. Kumar, A. Ahmad, D. K. Prasad, P. Senthilkumaran, B. S. Ahluwalia, and D. S. Mehta

High space-bandwidth in quantitative phase imaging using partially spatially coherent digital holographic microscopy and a deep neural network
Opt. Express 28(24), 36229 (2020).

  • Y. Jiao, Y. R. He, M. E. Kandel, X. Liu, W. Lu, and G. Popescu

Computational interference microscopy enabled by deep learning
APL Photonics 6(4), 046103 (2021).

  • D. Ryu, D. Ryu, Y. Baek, H. Cho, G. Kim, Y. S. Kim, Y. Lee, Y. Kim, J. C. Ye, H.-S. Min, and Y. Park

DeepRegularizer: Rapid Resolution Enhancement of Tomographic Imaging Using Deep Learning
IEEE Trans. Med. Imaging 40(5), 1508–1518 (2021).

  • Z. Meng, G. Pedrini, X. Lv, J. Ma, S. Nie, and C. Yuan

DL-SI-DHM: a deep network generating the high-resolution phase and amplitude images from wide-field images
Opt. Express 29(13), 19247 (2021).

  • A.-C. Li, S. Vyas, Y.-H. Lin, Y.-Y. Huang, H.-M. Huang, and Y. Luo

Patch-Based U-Net Model for Isotropic Quantitative Differential Phase Contrast Imaging
IEEE Trans. Med. Imaging 40(11), 3229–3237 (2021).

  • R. K. Gupta, N. Hempler, G. P. A. Malcolm, K. Dholakia, and S. J. Powis

High throughput hemogram of T cells using digital holographic microscopy and deep learning
Opt. Continuum 2(3), 670 (2023).

  • L. Wu, S. Bak, Y. H. Shin, Y. S. Chu, S. Yoo, I. K. Robinson and X. J. Huang

Resolution-enhanced X-ray fluorescence microscopy via deep residual networks
Npj Computational Materials 9, 43 (2023).

Aberration correction

  • T. Nguyen, V. Bui, V. Lam, C. B. Raub, L.-C. Chang, and G. Nehmetallah

Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection
Opt. Express 25(13), 15043 (2017).

  • G. Zhang, T. Guan, Z. Shen, X. Wang, T. Hu, D. Wang, Y. He, and N. Xie

Fast phase retrieval in off-axis digital holographic microscopy through deep learning
Opt. Express 26(15), 19388 (2018).

  • W. Xiao, L. Xin, R. Cao, X. Wu, R. Tian, L. Che, L. Sun, P. Ferraro, and F. Pan

Sensing morphogenesis of bone cells under microfluidic shear stress by holographic microscopy and automatic aberration compensation with deep learning
Lab Chip 21(7), 1385–1394 (2021).

  • S. Ma, R. Fang, Y. Luo, Q. Liu, S. Wang, and X. Zhou

Phase-aberration compensation via deep learning in digital holographic microscopy
Meas. Sci. Technol. 32(10), 105203 (2021).

  • L.-C. Lin, C.-H. Huang, Y.-F. Chen, D. Chu, and C.-J. Cheng

Deep learning-assisted wavefront correction with sparse data for holographic tomography
Optics and Lasers in Engineering 154, 107010 (2022).

Phase unwrapping

(Deep-learning-performed regression method, dRG)

  • G. Dardikman and N. T. Shaked

Phase Unwrapping Using Residual Neural Networks
in Imaging and Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&C, MATH, PcAOP) (OSA, 2018), p. CW3B.5.

  • G. Dardikman, N. A. Turko, and N. T. Shaked

Deep learning approaches for unwrapping phase images with steep spatial gradients: a simulation
in 2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE) (IEEE, 2018), pp. 1–4.

  • K. Wang, Y. Li, Q. Kemao, J. Di, and J. Zhao

One-step robust deep learning phase unwrapping
Opt. Express 27(10), 15100 (2019).

  • J. J. He, C. Sandino, D. Zeng, S. Vasanawala, and J. Cheng

Deep spatiotemporal phase unwrapping of phase-contrast MRI data
in Proceedings of the 27th ISMRM Annual Meeting & Exhibition, Montréal, QC, Canada (2019), pp. 11–16.

  • K. Ryu, S.-M. Gho, Y. Nam, K. Koch, and D.-H. Kim

Development of a deep learning method for phase unwrapping MR images
in Proc. Intl. Soc. Mag. Reson. Med. (2019), 27, p. ,4707.

  • G. Dardikman, D. Roitshtain, S. K. Mirsky, N. A. Turko, M. Habaza, and N. T. Shaked

PhUn-Net: ready-to-use neural network for unwrapping quantitative phase images of biological cells
Biomed. Opt. Express 11(2), 1107 (2020).

  • Y. Qin, S. Wan, Y. Wan, J. Weng, W. Liu, and Q. Gong

Direct and accurate phase unwrapping with deep neural network
Appl. Opt. 59(24), 7258 (2020).

  • M. V. Perera and A. De Silva

A Joint Convolutional and Spatial Quad-Directional LSTM Network for Phase Unwrapping
in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2021), pp. 4055–4059.

  • H. Zhou, C. Cheng, H. Peng, D. Liang, X. Liu, H. Zheng, and C. Zou

The PHU‐NET: A robust phase unwrapping method for MRI based on deep learning
Magn Reson Med 86(6), 3321–3333 (2021).

  • S. Park, Y. Kim, and I. Moon

Automated phase unwrapping in digital holography with deep learning
Biomed. Opt. Express 12(11), 7064 (2021).

  • L. Zhou, H. Yu, V. Pascazio, and M. Xing

PU-GAN: A One-Step 2-D InSAR Phase Unwrapping Based on Conditional Generative Adversarial Network
IEEE Trans. Geosci. Remote Sensing 60, 1–10 (2022).

  • M. Xu, C. Tang, Y. Shen, N. Hong, and Z. Lei

PU-M-Net for phase unwrapping with speckle reduction and structure protection in ESPI
Optics and Lasers in Engineering 151, 106824 (2022).

  • X. Xie, X. Tian, Z. Shou, Q. Zeng, G. Wang, Q. Huang, M. Qin, and X. Gao

Deep learning phase-unwrapping method based on adaptive noise evaluation
Appl. Opt. 61(23), 6861 (2022).

  • J. Zhao, L. Liu, T. Wang, X. Wang, X. Du, R. Hao, J. Liu, Y. Liu, and J. Zhang

VDE-Net: a two-stage deep learning method for phase unwrapping
Opt. Express 30(22), 39794 (2022).

(Deep-learning-performed wrap count method, dWC)

  • G. E. Spoorthi, S. Gorthi, and R. K. S. S. Gorthi

PhaseNet: A Deep Convolutional Neural Network for Two-Dimensional Phase Unwrapping
IEEE Signal Process. Lett. 26(1), 54–58 (2018).

  • G. Dardikman, N. A. Turko, and N. T. Shaked

Deep learning approaches for unwrapping phase images with steep spatial gradients: a simulation
in 2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE) (IEEE, 2018), pp. 1–4.

  • J. Zhang, X. Tian, J. Shao, H. Luo, and R. Liang

Phase unwrapping in optical metrology via denoised and convolutional segmentation networks
Opt. Express 27(10), 14903 (2019).

  • T. Zhang, S. Jiang, Z. Zhao, K. Dixit, X. Zhou, J. Hou, Y. Zhang, and C. Yan

Rapid and robust two-dimensional phase unwrapping via deep learning
Opt. Express 27(16), 23173 (2019).

  • G. E. Spoorthi, R. K. Sai Subrahmanyam Gorthi, and S. Gorthi

PhaseNet 2.0: Phase Unwrapping of Noisy Data Based on Deep Learning Approach
IEEE Trans. on Image Process. 29, 4862–4872 (2020).

  • C. Wu, Z. Qiao, N. Zhang, X. Li, J. Fan, H. Song, D. Ai, J. Yang, and Y. Huang

Phase unwrapping based on a residual en-decoder network for phase images in Fourier domain Doppler optical coherence tomography
Biomed. Opt. Express 11(4), 1760 (2020).

  • Z. Zhao, B. Li, X. Kang, J. Lu, and T. Liu

Phase unwrapping method for point diffraction interferometer based on residual auto encoder neural network
Optics and Lasers in Engineering 138, 106405 (2020).

  • S. Zhu, Z. Zang, X. Wang, Y. Wang, X. Wang, and D. Liu

Phase unwrapping in ICF target interferometric measurement via deep learning
Appl. Opt. 60(1), 10 (2021).

  • K. S. Vengala, N. Paluru, and R. K. S. Subrahmanyam Gorthi

3D deformation measurement in digital holographic interferometry using a multitask deep learning architecture
J. Opt. Soc. Am. A 39(1), 167 (2022).

  • K. S. Vengala, V. Ravi, and G. R. K. Sai Subrahmanyam

A Multi-task Learning for 2D Phase Unwrapping in Fringe Projection
IEEE Signal Process. Lett. 29, 797–801 (2022).

  • J. Zhang and Q. Li

EESANet: edge-enhanced self-attention network for two-dimensional phase unwrapping
Opt. Express 30(7), 10470 (2022).

  • W. Huang, X. Mei, Y. Wang, Z. Fan, C. Chen, and G. Jiang

Two-dimensional phase unwrapping by a high-resolution deep learning network
Measurement 200, 111566 (2022).

  • Y. Wang, C. Zhou, and X. Qi

PEENet for phase unwrapping in fringe projection profilometry
in Thirteenth International Conference on Information Optics and Photonics (CIOP 2022), Y. Yang, ed. (SPIE, 2022), p. 163.

(Deep-learning-assisted method, dAS)

  • W. Schwartzkopf, T. E. Milner, J. Ghosh, B. L. Evans, and A. C. Bovik

Two-dimensional phase unwrapping using neural networks
in 4th IEEE Southwest Symposium on Image Analysis and Interpretation (IEEE Comput. Soc, 2000), pp. 274–277.

  • L. Zhou, H. Yu, and Y. Lan

Deep Convolutional Neural Network-Based Robust Phase Gradient Estimation for Two-Dimensional Phase Unwrapping Using SAR Interferograms
IEEE Trans. Geosci. Remote Sensing 58(7), 4653–4665 (2020).

  • F. Sica, F. Calvanese, G. Scarpa, and P. Rizzoli

A CNN-Based Coherence-Driven Approach for InSAR Phase Unwrapping
IEEE Geosci. Remote Sensing Lett. 19, 1–5 (2020).

  • Z. Wu, T. Wang, Y. Wang, and D. Ge

A New Phase Unwrapping Method Combining Minimum Cost Flow with Deep Learning
in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (IEEE, 2021), pp. 3177–3180.

  • H. Wang, J. Hu, H. Fu, C. Wang, and Z. Wang

A Novel Quality-Guided Two-Dimensional InSAR Phase Unwrapping Method via GAUNet
IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing 14, 7840–7856 (2021).

  • L. Zhou, H. Yu, Y. Lan, and M. Xing

Deep Learning-Based Branch-Cut Method for InSAR Two-Dimensional Phase Unwrapping
IEEE Trans. Geosci. Remote Sensing 60, 1–15 (2021).

  • Z. Wu, T. Wang, Y. Wang, R. Wang, and D. Ge

Deep-Learning-Based Phase Discontinuity Prediction for 2-D Phase Unwrapping of SAR Interferograms
IEEE Trans. Geosci. Remote Sensing 60, 1–16 (2021).

  • L. Li, H. Zhang, Y. Tang, C. Wang, and F. Gu

InSAR Phase Unwrapping by Deep Learning Based on Gradient Information Fusion
IEEE Geosci. Remote Sensing Lett. 19, 1–5 (2021).

Back to Top

Deep learning for phase processing

Segmentation

  • T. H. Nguyen, S. Sridharan, V. Macias, A. Kajdacsy-Balla, J. Melamed, M. N. Do, and G. Popescu

Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning
J. Biomed. Opt 22(3), 036015 (2017).

  • F. Yi, I. Moon, and B. Javidi

Automated red blood cells extraction from holographic images using fully convolutional neural networks
Biomed. Opt. Express 8(10), 4466 (2017).

  • J. Lee, H. Kim, H. Cho, Y. Jo, Y. Song, D. Ahn, K. Lee, Y. Park, and S.-J. Ye

Deep-Learning-Based Label-Free Segmentation of Cell Nuclei in Time-Lapse Refractive Index Tomograms
IEEE Access 7, 83449–83460 (2019).

  • E. Ahmadzadeh, K. Jaferzadeh, S. Shin, and I. Moon

Automated single cardiomyocyte characterization by nucleus extraction from dynamic holographic images using a fully convolutional neural network
Biomed. Opt. Express 11(3), 1501 (2020).

  • M. E. Kandel, M. Rubessa, Y. R. He, S. Schreiber, S. Meyers, L. Matter Naves, M. K. Sermersheim, G. S. Sell, M. J. Szewczyk, N. Sobh, M. B. Wheeler, and G. Popescu

Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure
Proc. Natl. Acad. Sci. U.S.A. 117(31), 18302–18309 (2020).

  • M. Lee, Y.-H. Lee, J. Song, G. Kim, Y. Jo, H. Min, C. H. Kim, and Y. Park

Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells
eLife 9, e49023 (2020).

  • J. Choi, H.-J. Kim, G. Sim, S. Lee, W. S. Park, J. H. Park, H.-Y. Kang, M. Lee, W. D. Heo, J. Choo, H. Min, and Y. Park

Label-free three-dimensional analyses of live cells with deep-learning-based segmentation exploiting refractive index distributions
Preprint at bioRxiv (2021).

  • N. Goswami, Y. R. He, Y.-H. Deng, C. Oh, N. Sobh, E. Valera, R. Bashir, N. Ismail, H. Kong, T. H. Nguyen, C. Best-Popescu, and G. Popescu

Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity
Light Sci. Appl. 10(1), 176 (2021).

  • C. Hu, S. He, Y. J. Lee, Y. He, E. M. Kong, H. Li, M. A. Anastasio, and G. Popescu

Live-dead assay on unlabeled cells using phase imaging with computational specificity
Nat. Commun. 13(1), 713 (2022).

  • J. K. Zhang, M. Fanous, N. Sobh, A. Kajdacsy-Balla, and G. Popescu

Automatic Colorectal Cancer Screening Using Deep Learning in Spatial Light Interference Microscopy Data
Cells 11(4), 716 (2022).

  • Y. R. He, S. He, M. E. Kandel, Y. J. Lee, C. Hu, N. Sobh, M. A. Anastasio, and G. Popescu

Cell Cycle Stage Classification Using Phase Imaging with Computational Specificity
ACS Photonics 9(4), 1264–1273 (2022).

  • S. Jiang, C. Guo, P. Song, T. Wang, R. Wang, T. Zhang, Q. Wu, R. Pandey, and G. Zheng

High-throughput digital pathology via a handheld, multiplexed, and AI-powered ptychographic whole slide scanner
Lab. Chip 22(14), 2657–2670 (2022).

Classification

(via conventional machine learning)

  • C. L. Chen, A. Mahjoubfar, L.-C. Tai, I. K. Blaby, A. Huang, K. R. Niazi, and B. Jalali

Deep Learning in Label-free Cell Classification
Sci Rep 6(1), 21471 (2016).

  • D. Roitshtain, L. Wolbromsky, E. Bal, H. Greenspan, L. L. Satterwhite, and N. T. Shaked

Quantitative phase microscopy spatial signatures of cancer cells
Cytometry 91(5), 482–493 (2017).

  • J. Yoon, Y. Jo, M. Kim, K. Kim, S. Lee, S.-J. Kang, and Y. Park

Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning
Sci Rep 7(1), 6654 (2017).

  • S. K. Mirsky, I. Barnea, M. Levi, H. Greenspan, and N. T. Shaked

Automated analysis of individual sperm cells using stain-free interferometric phase microscopy and machine learning: Sperm Analysis Using Interferometry and Machine Learning
Cytometry 91(9), 893–900 (2017).

  • Y. Li, B. Cornelis, A. Dusa, G. Vanmeerbeeck, D. Vercruysse, E. Sohn, K. Blaszkiewicz, D. Prodanov, P. Schelkens, and L. Lagae

Accurate label-free 3-part leukocyte recognition with single cell lens-free imaging flow cytometry
Computers in Biology and Medicine 96, 147–156 (2018).

  • B. Javidi, A. Markman, S. Rawat, T. O’Connor, A. Anand, and B. Andemariam

Sickle cell disease diagnosis based on spatio-temporal cell dynamics analysis using 3D printed shearing digital holographic microscopy
Opt. Express 26(10), 13614 (2018).

  • G. Kim, Y. Jo, H. Cho, H. Min, and Y. Park

Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells
Biosensors and Bioelectronics 123, 69–76 (2019).

  • Y. Ozaki, H. Yamada, H. Kikuchi, A. Hirotsu, T. Murakami, T. Matsumoto, T. Kawabata, Y. Hiramatsu, K. Kamiya, T. Yamauchi, K. Goto, Y. Ueda, S. Okazaki, M. Kitagawa, H. Takeuchi, and H. Konno

Label-free classification of cells based on supervised machine learning of subcellular structures
PLoS ONE 14(1), e0211347 (2019).

  • V. Bianco, P. Memmolo, P. Carcagnì, F. Merola, M. Paturzo, C. Distante, and P. Ferraro

Microplastic Identification via Holographic Imaging and Machine Learning
Advanced Intelligent Systems 2(2), 1900153 (2020).

  • A. V. Belashov, A. A. Zhikhoreva, T. N. Belyaeva, E. S. Kornilova, A. V. Salova, I. V. Semenova, and O. S. Vasyutinskii

In vitro monitoring of photoinduced necrosis in HeLa cells using digital holographic microscopy and machine learning
J. Opt. Soc. Am. A 37(2), 346 (2020).

  • V. K. Lam, T. C. Nguyen, V. Bui, B. M. Chung, L.-C. Chang, G. Nehmetallah, and C. B. Raub

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging
J. Biomed. Opt. 25(02), 026002–026002 (2020).

  • S. Park, J. W. Ahn, Y. Jo, H.-Y. Kang, H. J. Kim, Y. Cheon, J. W. Kim, Y. Park, S. Lee, and K. Park

Label-Free Tomographic Imaging of Lipid Droplets in Foam Cells for Machine-Learning-Assisted Therapeutic Evaluation of Targeted Nanodrugs
ACS Nano 14(2), 1856–1865 (2020).

  • N. Nissim, M. Dudaie, I. Barnea, and N. T. Shaked

Real‐Time Stain‐Free Classification of Cancer Cells and Blood Cells Using Interferometric Phase Microscopy and Machine Learning
Cytometry 99(5), 511–523 (2021).

  • V. Bianco, D. Pirone, P. Memmolo, F. Merola, and P. Ferraro

Identification of Microplastics Based on the Fractal Properties of Their Holographic Fingerprint
ACS Photonics 8(7), 2148–2157 (2021).

  • S. K. Paidi, P. Raj, R. Bordett, C. Zhang, S. H. Karandikar, R. Pandey, and I. Barman

Raman and quantitative phase imaging allow morpho-molecular recognition of malignancy and stages of B-cell acute lymphoblastic leukemia
Biosensors and Bioelectronics 190, 113403 (2021).

  • P. Memmolo, G. Aprea, V. Bianco, R. Russo, I. Andolfo, M. Mugnano, F. Merola, L. Miccio, A. Iolascon, and P. Ferraro

Differential diagnosis of hereditary anemias from a fraction of blood drop by digital holography and hierarchical machine learning
Biosensors and Bioelectronics 201, 113945 (2022).

  • M. Valentino, J. Bĕhal, V. Bianco, S. Itri, R. Mossotti, G. D. Fontana, T. Battistini, E. Stella, L. Miccio, and P. Ferraro

Intelligent polarization-sensitive holographic flow-cytometer: Towards specificity in classifying natural and microplastic fibers
Science of The Total Environment 815, 152708 (2022).

  • D. Pirone, L. Xin, V. Bianco, L. Miccio, W. Xiao, L. Che, X. Li, P. Memmolo, F. Pan, and P. Ferraro

Identification of drug-resistant cancer cells in flow cytometry combining 3D holographic tomography with machine learning
Sensors and Actuators B: Chemical 375, 132963 (2023).

(via deep learning with only phase as input)

  • Y. Jo, S. Park, J. Jung, J. Yoon, H. Joo, M. Kim, S.-J. Kang, M. C. Choi, S. Y. Lee, and Y. Park

Holographic deep learning for rapid optical screening of anthrax spores
Sci. Adv. 3(8), e1700606 (2017).

  • S. H. Karandikar, C. Zhang, A. Meiyappan, I. Barman, C. Finck, P. K. Srivastava, and R. Pandey

Reagent-Free and Rapid Assessment of T Cell Activation State Using Diffraction Phase Microscopy and Deep Learning
Anal. Chem. 91(5), 3405–3411 (2019).

  • M. Rubin

TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set
Medical Image Analysis 57, 176–185 (2019).

  • J. K. Zhang, Y. R. He, and N. Sobh

Label-free colorectal cancer screening using deep learning and spatial light interference microscopy (SLIM)
APL Photonics 5(4), 040805 (2020).

  • A. Butola, D. Popova, D. K. Prasad, A. Ahmad, A. Habib, J. C. Tinguely, P. Basnet, G. Acharya, P. Senthilkumaran, D. S. Mehta, and B. S. Ahluwalia

High spatially sensitive quantitative phase imaging assisted with deep neural network for classification of human spermatozoa under stressed condition
Sci Rep 10(1), 13118 (2020).

  • Y. Li, J. Di, L. Ren, and J. Zhao

Deep-learning-based prediction of living cells mitosis via quantitative phase microscopy
Chin. Opt. Lett. 19(5), 051701 (2021).

  • X. Shu, S. Sansare, D. Jin, X. Zeng, K.-Y. Tong, R. Pandey, and R. Zhou

Artificial‐Intelligence‐Enabled Reagent‐Free Imaging Hematology Analyzer
Advanced Intelligent Systems 3(8), 2000277 (2021).

  • B. L. Reddy, R. N. Uma Mahesh, and A. Nelleri

Deep convolutional neural network for three-dimensional objects classification using off-axis digital Fresnel holography
Journal of Modern Optics 69(13), 705–717 (2022).

(via deep learning with phase and amplitude as input)

  • T. Pitkäaho, A. Manninen, and T. J. Naughton

Temporal Deep Learning Classification of Digital Hologram Reconstructions of Multicellular Samples
in Biophotonics Congress: Biomedical Optics Congress 2018 (Microscopy/Translational/Brain/OTS) (OSA, 2018), p. JW3A.14.

  • Y. Wu, A. Calis, Y. Luo, C. Chen, M. Lutton, Y. Rivenson, X. Lin, H. C. Koydemir, Y. Zhang, H. Wang, Z. Göröcs, and A. Ozcan

Label-Free Bioaerosol Sensing Using Mobile Microscopy and Deep Learning
ACS Photonics 5(11), 4617–4627 (2018).

  • H. H. Lam, P. W. M. Tsang, and T.-C. Poon

Ensemble convolutional neural network for classifying holograms of deformable objects
Opt. Express 27(23), 34050 (2019).

  • H. H. S. Lam, P. W. M. Tsang, and T.-C. Poon

Hologram classification of occluded and deformable objects with speckle noise contamination by deep learning
J. Opt. Soc. Am. A 39(3), 411 (2022).

  • D. Terbe, L. Orzó, and Á. Zarándy

Classification of Holograms with 3D-CNN
Sensors 22(21), 8366 (2022).

  • H. Lam, Y. Zhu, and P. Buranasiri

Off-Axis Holographic Interferometer with Ensemble Deep Learning for Biological Tissues Identification
Applied Sciences 12(24), 12674 (2022).

(via deep learning with multi-wavelength phase as input)

  • N. Singla and V. Srivastava

Deep learning enabled multi-wavelength spatial coherence microscope for the classification of malaria-infected stages with limited labelled data size
Optics & Laser Technology 130, 106335 (2020).

  • Ç. Işıl, K. de Haan, Z. Göröcs, H. C. Koydemir, S. Peterman, D. Baum, F. Song, T. Skandakumar, E. Gumustekin, and A. Ozcan

Phenotypic Analysis of Microalgae Populations Using Label-Free Imaging Flow Cytometry and Deep Learning
ACS Photonics 8(4), 1232–1242 (2021).

(via deep learning with multi-temporal-dimension phase as input)

  • H. Wang, H. Ceylan Koydemir, Y. Qiu, B. Bai, Y. Zhang, Y. Jin, S. Tok, E. C. Yilmaz, E. Gumustekin, Y. Rivenson, and A. Ozcan

Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning
Light Sci Appl 9(1), 118 (2020).

  • S. Ben Baruch, N. Rotman-Nativ, A. Baram, H. Greenspan, and N. T. Shaked

Cancer-Cell Deep-Learning Classification by Integrating Quantitative-Phase Spatial and Temporal Fluctuations
Cells 10(12), 3353 (2021).

  • T. Liu, Y. Li, H. C. Koydemir, Y. Zhang, E. Yang, H. Wang, J. Li, B. Bai, and A. Ozcan

Stain-free, rapid, and quantitative viral plaque assay using deep learning and holography
Preprint at arXiv (2022).

(via deep learning with 3D refractive index as input)

  • D. Ryu, J. Kim, D. Lim, H.-S. Min, I. Y. Yoo, D. Cho, and Y. Park

Label-Free White Blood Cell Classification Using Refractive Index Tomography and Deep Learning
BME Front 2021, 2021/9893804 (2021).

  • G. Kim, D. Ahn, M. Kang, J. Park, D. Ryu, Y. Jo, J. Song, J. S. Ryu, G. Choi, H. J. Chung, K. Kim, D. R. Chung, I. Y. Yoo, H. J. Huh, H. Min, N. Y. Lee, and Y. Park

Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network
Light Sci Appl 11(1), 190 (2022).

(via deep learning with amplitude or hologram as input)

  • S.-J. Kim, C. Wang, B. Zhao, H. Im, J. Min, H. J. Choi, J. Tadros, N. R. Choi, C. M. Castro, R. Weissleder, H. Lee, and K. Lee

Deep transfer learning-based hologram classification for molecular diagnostics
Sci Rep 8(1), 17003 (2018).

  • Y. Zhu, C. Hang Yeung, and E. Y. Lam

Digital holographic imaging and classification of microplastics using deep transfer learning
Appl. Opt. 60(4), A38 (2021).

  • M. Delli Priscoli, P. Memmolo, G. Ciaparrone, V. Bianco, F. Merola, L. Miccio, F. Bardozzo, D. Pirone, M. Mugnano, F. Cimmino, M. Capasso, A. Iolascon, P. Ferraro, and R. Tagliaferri

Neuroblastoma Cells Classification Through Learning Approaches by Direct Analysis of Digital Holograms
IEEE J. Select. Topics Quantum Electron. 27(5), 1–9 (2021).

  • Y. Zhu, C. Hang Yeung, and E. Y. Lam

Microplastic pollution monitoring with holographic classification and deep learning
J. Phys. Photonics 3(2), 024013 (2021).

  • D. Chen, Z. Wang, K. Chen, Q. Zeng, L. Wang, X. Xu, J. Liang, and X. Chen

Classification of unlabeled cells using lensless digital holographic images and deep neural networks
Quant Imaging Med Surg 11(9), 4137–4148 (2021).

  • L. MacNeil, S. Missan, J. Luo, T. Trappenberg, and J. LaRoche

Plankton classification with high-throughput submersible holographic microscopy and transfer learning
BMC Ecol Evo 21(1), 123 (2021).

  • Y. Zhu, H. K. A. Lo, C. H. Yeung, and E. Y. Lam

Microplastic pollution assessment with digital holography and zero-shot learningt
APL Photonics 7(7), 076102 (2022).

Imaging modal transformation

(Phase to bright-field or stained bright-field images)

  • Y. Wu, Y. Luo, G. Chaudhari, Y. Rivenson, A. Calis, K. de Haan, and A. Ozcan

Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram
Light Sci Appl 8(1), 25 (2019).

  • T. Liu, Z. Wei, Y. Rivenson, K. Haan, Y. Zhang, Y. Wu, and A. Ozcan

Deep learning‐based color holographic microscopy
J. Biophotonics 12(11), e201900107 (2019).

  • Y. Rivenson, T. Liu, Z. Wei, Y. Zhang, K. de Haan, and A. Ozcan

PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning
Light Sci Appl 8(1), 23 (2019).

  • Y. N. Nygate, M. Levi, S. K. Mirsky, N. A. Turko, M. Rubin, I. Barnea, G. Dardikman-Yoffe, M. Haifler, A. Shalev, and N. T. Shaked

Holographic virtual staining of individual biological cells
Proc. Natl. Acad. Sci. U.S.A. 117(17), 9223–9231 (2020).

  • R. Wang, P. Song, S. Jiang, C. Yan, J. Zhu, C. Guo, Z. Bian, T. Wang, and G. Zheng

Virtual brightfield and fluorescence staining for Fourier ptychography via unsupervised deep learning
Opt. Lett. 45(19), 5405 (2020).

  • D. Terbe, L. Orzó, and Á. Zarándy

Deep-learning-based bright-field image generation from a single hologram using an unpaired dataset
Opt. Lett. 46(22), 5567 (2021).

(Phase to fluorescence images)

  • S.-M. Guo, L.-H. Yeh, J. Folkesson, I. E. Ivanov, A. P. Krishnan, M. G. Keefe, E. Hashemi, D. Shin, B. B. Chhun, N. H. Cho, M. D. Leonetti, M. H. Han, T. J. Nowakowski, and S. B. Mehta

Revealing architectural order with quantitative label-free imaging and deep learning
eLife 9, e55502 (2020).

  • M. E. Kandel, Y. R. He, Y. J. Lee, T. H.-Y. Chen, K. M. Sullivan, O. Aydin, M. T. A. Saif, H. Kong, N. Sobh, and G. Popescu

Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments
Nat Commun 11(1), 6256 (2020).

  • M. E. Kandel, E. Kim, Y. J. Lee, G. Tracy, H. J. Chung, and G. Popescu

Multiscale Assay of Unlabeled Neurite Dynamics Using Phase Imaging with Computational Specificity
ACS Sens. 6(5), 1864–1874 (2021).

  • S. Guo, Y. Ma, Y. Pan, Z. J. Smith, and K. Chu

Organelle-specific phase contrast microscopy enables gentle monitoring and analysis of mitochondrial network dynamics
Biomed. Opt. Express 12(7), 4363 (2021).

  • X. Chen, M. E. Kandel, S. He, C. Hu, Y. J. Lee, K. Sullivan, G. Tracy, H. J. Chung, H. J. Kong, M. Anastasio, and G. Popescu

Artificial confocal microscopy for deep label-free imaging
Preprint at /arXiv (2021).

  • X. Chen, M. E. Kandel, S. He, C. Hu, Y. J. Lee, K. Sullivan, G. Tracy, H. J. Chung, H. J. Kong, M. Anastasio, and G. Popescu

Artificial confocal microscopy for deep label-free imaging
Nat. Photon. 17(3), 250–258 (2023).

(3D refractive index to fluorescence images)

  • Y. Jo, H. Cho, W. S. Park, G. Kim, D. Ryu, Y. S. Kim, M. Lee, S. Park, M. J. Lee, H. Joo, H. Jo, S. Lee, S. Lee, H. Min, W. D. Heo, and Y. Park

Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning
Nat Cell Biol 23(12), 1329–1337 (2021).

Back to Top

Review/Tutorial papers

(In chronological order)

Conventional phase recovery

  • Y. Shechtman, Y. C. Eldar, O. Cohen, H. N. Chapman, J. Miao, and M. Segev

Phase Retrieval with Application to Optical Imaging: A contemporary overview,
IEEE Signal Process. Mag. 32(3), 87–109 (2015).

  • Y. Park, C. Depeursinge, and G. Popescu

Quantitative phase imaging in biomedicine,
Nature Photon. 12(10), 578–589 (2018).

  • T. Latychevskaia

Iterative phase retrieval for digital holography: tutorial,
J. Opt. Soc. Am. A 36(12), D31 (2019).

  • H. Yu, Y. Lan, Z. Yuan, J. Xu, and H. Lee

Phase Unwrapping in InSAR: A Review,
IEEE Geosci. Remote Sens. Mag. 7(1), 40–58 (2019).

  • T. Cacace, V. Bianco, and P. Ferraro

Quantitative phase imaging trends in biomedical applications,
Optics and Lasers in Engineering 135, 106188 (2020).

  • J. T. Sheridan, R. K. Kostuk, A. F. Gil, Y. Wang, W. Lu, H. Zhong, Y. Tomita, C. Neipp, J. Francés, S. Gallego, I. Pascual, V. Marinova, S.-H. Lin, K.-Y. Hsu, F. Bruder, S. Hansen, C. Manecke, R. Meisenheimer, C. Rewitz, T. Rölle, S. Odinokov, O. Matoba, M. Kumar, X. Quan, Y. Awatsuji, P. W. Wachulak, A. V. Gorelaya, A. A. Sevryugin, E. V. Shalymov, V. Yu Venediktov, R. Chmelik, M. A. Ferrara, G. Coppola, A. Márquez, A. Beléndez, W. Yang, R. Yuste, A. Bianco, A. Zanutta, C. Falldorf, J. J. Healy, X. Fan, B. M. Hennelly, I. Zhurminsky, M. Schnieper, R. Ferrini, S. Fricke, G. Situ, H. Wang, A. S. Abdurashitov, V. V. Tuchin, N. V. Petrov, T. Nomura, D. R. Morim, and K. Saravanamuttu

Roadmap on holography,
J. Opt. 22(12), 123002 (2020).

  • C. Zuo, J. Li, J. Sun, Y. Fan, J. Zhang, L. Lu, R. Zhang, B. Wang, L. Huang, and Q. Chen

Transport of intensity equation: a tutorial,
Optics and Lasers in Engineering 106187 (2020).

  • V. Balasubramani, M. Kujawińska, C. Allier, V. Anand, C.-J. Cheng, C. Depeursinge, N. Hai, S. Juodkazis, J. Kalkman, A. Kuś, M. Lee, P. J. Magistretti, P. Marquet, S. H. Ng, J. Rosen, Y. K. Park, and M. Ziemczonok

Roadmap on Digital Holography-Based Quantitative Phase Imaging,
J. Imaging 7(12), 252 (2021).

  • B. Javidi, A. Carnicer, A. Anand, G. Barbastathis, W. Chen, P. Ferraro, J. W. Goodman, R. Horisaki, K. Khare, M. Kujawinska, R. A. Leitgeb, P. Marquet, T. Nomura, A. Ozcan, Y. Park, G. Pedrini, P. Picart, J. Rosen, G. Saavedra, N. T. Shaked, A. Stern, E. Tajahuerce, L. Tian, G. Wetzstein, and M. Yamaguchi

Roadmap on digital holography [Invited],
Opt. Express 29(22), 35078 (2021).

  • G. Zheng, C. Shen, S. Jiang, P. Song, and C. Yang

Concept, implementations and applications of Fourier ptychography,
Nat. Rev. Phys. 3(3), 207–223 (2021).

  • V. Petrov, A. Pogoda, V. Sementin, A. Sevryugin, E. Shalymov, D. Venediktov, and V. Venediktov

Advances in Digital Holographic Interferometry,
J. Imaging 8(7), 196 (2022).

  • T. L. Nguyen, S. Pradeep, R. L. Judson-Torres, J. Reed, M. A. Teitell, and T. A. Zangle

Quantitative Phase Imaging: Recent Advances and Expanding Potential in Biomedicine,
ACS Nano 16(8), 11516–11544 (2022).

  • G. Baffou

Wavefront Microscopy Using Quadriwave Lateral Shearing Interferometry: From Bioimaging to Nanophotonics,
ACS Photonics 10(2), 322–339 (2023).

Deep-learning-based phase recovery

  • Y. Jo, H. Cho, S. Y. Lee, G. Choi, G. Kim, H. Min, and Y. Park

Quantitative Phase Imaging and Artificial Intelligence: A Review,
IEEE J. Select. Topics Quantum Electron. 25(1), 1–14 (2019).

  • Y. Rivenson, Y. Wu, and A. Ozcan

Deep learning in holography and coherent imaging,
Light Sci. Appl. 8(1), 85 (2019).

  • G. Ongie, A. Jalal, C. A. Metzler, R. G. Baraniuk, A. G. Dimakis, and R. Willett

Deep Learning Techniques for Inverse Problems in Imaging,
arXiv:2005.06001 (2020).

  • T. Zeng, Y. Zhu, and E. Y. Lam

Deep learning for digital holography: a review,
Opt. Express 29(24), 40572 (2021).

  • L. Zhou, H. Yu, Y. Lan, and M. Xing

Artificial Intelligence In Interferometric Synthetic Aperture Radar Phase Unwrapping: A Review,
IEEE Geosci. Remote Sens. Mag. 2–20 (2021).

  • C. Zuo, J. Qian, S. Feng, W. Yin, Y. Li, P. Fan, J. Han, K. Qian, and Q. Chen

Deep learning in optical metrology: a review,
Light Sci. Appl. 11(1), 39 (2022).

  • T. Shimobaba, D. Blinder, T. Birnbaum, I. Hoshi, H. Shiomi, P. Schelkens, and T. Ito

Deep-Learning Computational Holography: A Review,
Front. Photon. 3, 854391 (2022).

  • G. Situ

Deep holography,
Light Advanced Manufacturing 3(2), 1 (2022).

  • A. Qayyum, I. Ilahi, F. Shamshad, F. Boussaid, M. Bennamoun, and J. Qadir

Untrained Neural Network Priors for Inverse Imaging Problems: A Survey,
IEEE Trans. Pattern Anal. Mach. Intell. 45(5), 6511–6536 (2022).

  • Y. Guo, L. Zhong, L. Min, J. Wang, Y. Wu, K. Chen, K. Wei, and C. Rao

Adaptive optics based on machine learning: a review,
Opto-Electronic Advances 5(7), 200082–200082 (2022).

  • K. Wang, Q. Kemao, J. Di, and J. Zhao

Deep learning spatial phase unwrapping: a comparative review,
Adv. Photon. Nexus 1(1), 014001 (2022).

  • J. Dong, L. Valzania, A. Maillard, T. Pham, S. Gigan, and M. Unser

Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial,
IEEE Signal Process. Mag. 40(1), 45–57 (2023).

  • J. Park, B. Bai, D. Ryu, T. Liu, C. Lee, Y. Luo, M. J. Lee, L. Huang, J. Shin, Y. Zhang, D. Ryu, Y. Li, G. Kim, H. Min, A. Ozcan, and Y. Park

Artificial intelligence-enabled quantitative phase imaging methods for life sciences,
Nat Methods 20, 1645–1660 (2023).

  • K. Wang, L. Song, C. Wang, Z. Ren, G. Zhao, J. Dou, J. Di, G. Barbastathis, R. Zhou, J. Zhao, and E. Y. Lam

On the use of deep learning for phase recovery,
Light Sci Appl 13(1), 4 (2024).

Back to Top

Books

(In chronological order)

  • D. C. Ghiglia and M. D. Pritt

Two-Dimensional Phase Unwrapping: Theory, Algorithms, and Software,
(Wiley, 1998).

  • J. W. Goodman

Introduction to Fourier Optics,
3rd ed (Roberts & Co, 2005).

  • E. H. Duke and S. R. Aguirre

3D Imaging: Theory, Technology and Applications, Computer Science, Technology and Applications,
(Nova Science Publishers, 2010).

  • T. Tishko, D. Tishko, and V. P. Titar

Holographic Microscopy of Phase Microscopic Objects: Theory and Practice,
(World Scientific Publishing Co., 2011).

  • M. Mir, B. Bhaduri, R. Wang, R. Zhu, and G. Popescu

Quantitative Phase Imaging,
in Progress in Optics (Elsevier, 2012), 57, pp. 133–217.

  • Q. Kemao

Windowed Fringe Pattern Analysis,
(SPIE, 2013).

  • B. Javidi, E. Tajahuerce, and P. Andres

Multi-Dimensional Imaging,
(Cambridge University Press, John Wiley & Sons Inc, 2014).

  • T.-C. Poon and J.-P. Liu

Introduction to Modern Digital Holography: With MATLAB,
(Cambridge University Press, 2014).

  • M. Servín, J. Antonio Quiroga, J. Moisés Padilla, J. A. Quiroga, J. M. Padilla, and J. M. Padilla

Fringe Pattern Analysis for Optical Metrology: Theory, Algorithms, and Applications,
(Wiley-VCH, 2014).

  • C. Liu, S. Wang, and S. P. Veetil

Computational Optical Phase Imaging, Progress in Optical Science and Photonics,
(Springer Singapore, 2022), 21.

Back to Top

Dissertations and Thesis

(In chronological order)

  • George Barbastathis,

Intelligent holographic databases,
Ph.D. Thesis, California Institute of Technology, 1997. PDF

  • Stephen A. Boppart,

Surgical diagnostics, guidance, and intervention using optical coherence tomography,
Ph.D. Thesis, Massachusetts Institute of Technology, 1998. PDF

  • Changhuei Yang,

Harmonic phase based low coherence interferometry : a method for studying the dynamics and structures of cells,
Ph.D. Thesis, Massachusetts Institute of Technology, 2002. PDF

  • Laura Waller,

Computational phase imaging based on intensity transport,
Ph.D. Thesis, Massachusetts Institute of Technology, 2010. PDF

  • Guoan Zheng,

Innovations in Imaging System Design: Gigapixel, Chip-Scale and MultiFunctional Microscopy,
Ph.D. Thesis, California Institute of Technology, 2012. PDF

  • YongKeun Park,

Pathophysiology of human red blood cell probed by quantitative phase microscopy,
Ph.D. Thesis, Massachusetts Institute of Technology, 2013. PDF

  • Hoa Pham,

Real-time quantitative phase imaging for cell studies,
Ph.D. Thesis, University of Illinois at Urbana-Champaign, 2013. PDF

  • Lei Tian,

Compressive phase retrieval,
Ph.D. Thesis, Massachusetts Institute of Technology, 2013. PDF

  • Mustafa Mir,

Quantitative phase imaging for cellular biology,
Ph.D. Thesis, University of Illinois at Urbana-Champaign, 2013. PDF

  • Renjie Zhou,

Interferometric light microscopy for wafer defect inspection and three-dimensional object reconstruction,
Ph.D. Thesis, University of Illinois at Urbana-Champaign, 2013. PDF

  • Nathan D. Shemonski,

In vivo human computed optical interferometric tomography,
Ph.D. Thesis, University of Illinois at Urbana-Champaign, 2015. PDF

  • Dennis J. Lee,

Computational optical imaging: Applications in synthetic aperture imaging, phase retrieval, and digital holography,
Ph.D. Thesis, Purdue University, 2015. PDF

  • Tae-Woo Kim,

Quantitative phase imaging: advances to 3D imaging and applications to neuroscience,
Ph.D. Thesis, University of Illinois at Urbana-Champaign, 2015. PDF

  • Chien-Hung Lu,

Computational Phase Imaging in Nonlinear and Quantum Systems,
Ph.D. Thesis, Princeton University, 2015. PDF

  • Shamira Sridharan,

Applications of quantitative phase imaging for pathology,
Ph.D. Thesis, University of Illinois at Urbana-Champaign, 2015. PDF

  • Tan Huu Nguyen,

Computational phase imaging for biomedical applications,
Ph.D. Thesis, University of Illinois at Urbana-Champaign, 2016. PDF

  • Chandrabhan Seniya,

A flexible low-cost quantitative phase imaging microscopy system for label-free imaging of multi-cellular biological samples,
Ph.D. Thesis, University of Warwick, 2018. PDF

  • Aamod Shanker,

Differential methods in phase imaging for optical lithography,
Ph.D. Thesis, University of California, Berkeley, 2018. PDF

  • Shuai Li,

Computational imaging through deep learning,
Ph.D. Thesis, Massachusetts Institute of Technology, 2019. PDF

  • David A. Barmherzig,

The Phase Retrieval Problem: Theory, Algorithms, and Applications,
Ph.D. Thesis, Stanford University, 2019. PDF

  • Yichen Wu,

Deep Learning-enabled Computational Imaging in Optical Microscopy and Air Quality Monitoring,
Ph.D. Thesis, University of California, Los Angeles, 2019. PDF

  • Michael Kellman,

Physics-based Learning for Large-scale Computational Imaging,
Ph.D. Thesis, University of California, Berkeley, 2020. PDF

  • Zhuoqun Zhang,

Analysis and Development of Phase Retrieval Algorithms for Ptychography,
Ph.D. Thesis, University of Sheffield, 2021. PDF

  • Baoliang Ge,

Single-shot quantitative interferometric microscopy for imaging high-speed dynamics,
Ph.D. Thesis, Massachusetts Institute of Technology, 2021. PDF

  • Obed A. Ayisi,

Multiple-Wavelength Phase Retrieval With Digital Holographic Microscopy,
Masters Thesis, Northern Arizona University, 2021.

  • Tairan Liu,

Deep Learning in Optical Microscopy, Holographic Imaging and Sensing,
Ph.D. Thesis, University of California, Los Angeles, 2022.

  • Marissa A. Morado,

Solving the phase retrieval problem using an artificial neural network,
Ph.D. Thesis, California State University, Fresno, 2022. PDF

Back to Top