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Research Article

Rapid quantitative phase imaging using deep learning for phase object with refractive index variation

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Pages 327-338 | Received 10 Aug 2020, Accepted 22 Feb 2021, Published online: 09 Mar 2021

References

  • Xu XQ, Wang YW, Ji Y, et al. A novel phase retrieval method from three-wavelength in-line phase-shifting interferograms based on positive negative 2π phase shifts. J Mod Opt. 2018;65:8–15.
  • Nguyen H, Nguyen D, Wang Z, et al. Real-time, high-accuracy 3D imaging and shape measurement. Appl Opt. 2015;54:A9–A17.
  • Xu XQ, Wang YW, Ji Y, et al. Quantitative phase imaging using four interferograms with special phase shifts by dual-wavelength in-line phase-shifting interferometry. J Mod Opt. 2018;65:1090–1097.
  • Xu XQ, Wang YW, Xu YY, et al. Dual-wavelength in-line phase-shifting interferometry based on two dc-term-suppressed intensities with a special phase shift for quantitative phase extraction. Opt Lett. 2016;41:2430–2433.
  • Yamaguchi I, Zhang T. Phase shifting digital holography. Opt Lett. 1997;22:1268–1270.
  • Warnasooriya N, Kim MK. LED-based multi-wavelength phase imaging interference microscopy. Opt Express. 2007;15:9239–9247.
  • Wang Z, Han B. Advanced iterative algorithm for phase extraction of randomly phase-shifted interferograms. Opt Lett. 2004;29:1671–1673.
  • Wang Z, Han B. Advanced iterative algorithm for randomly phase-shifted interferograms with intra-and inter-frame intensity variations. Opt Lasers Eng. 2007;45:274–280.
  • Fei L, Lu X, Wang H, et al. Single-wavelength phase retrieval method from simultaneous multi-wavelength in-line phase-shifting interferograms. Opt Express. 2014;22:30910–30923.
  • Xu XQ, Wang YW, Ji Y, et al. A novel dual-wavelength iterative method for generalized dual-wavelength phase-shifting interferometry with second-order harmonics. Opt Lasers Eng. 2018;106:39–46.
  • Deng J, Wang H, Zhang F, et al. Two-step phase demodulation algorithm based on the extreme value of interference. Opt Lett. 2012;37:4669–4671.
  • Vargas J, Quiroga JA, Sorzano CO, et al. Two-step demodulation based on the Gram-Schmidt orthonormalization method. Opt Lett. 2012;37:443–445.
  • Zhao M, Huang L, Zhang Q, et al. Quality-guided phase-unwrapping technique: comparison of quality maps and guiding strategies. Appl Opt. 2011;50:6214–6224.
  • McClulloh WS, Pitts W. A logical calculus of the ideas immanent in neurons activity. Bull Math Biophys. 1943;5:115–113.
  • LeCun Y, Bengio Y, Hinton G. Deep learning; 16 Gradient-based learning applied to document recognition. Nature. 2015;521:436–444.
  • LeCun Y, Bottou L, Bengio Y, et al. Proc IEEE. 1998;86:2278–2324.
  • Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, editors. International conference on Neural Information Processing systems; Morehouse Lane, Red Hook, NY: Curran Associates Inc.; 2012. p. 1097–1105.
  • Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39:640–651.
  • Webb S. Deep learning For Biology. Nature. 2018;554:555–557.
  • Falk T, Mai D, Bensch R, et al. U-Net: deep learning for cell counting, detection, and morphometry. Nat Methods. 2019;16:67–70.
  • Sinha A, Lee J, Li S, et al. Lensless computational imaging through deep learning. Optica. 2017;4:1117–1125.
  • Goy A, Arthur K, Li S, et al. Low Photon Count Phase Retrieval Using Deep Learning. Phys Rev Lett. 2018;121:243902.
  • Hand P, Leong O, Voroninski V. Phase retrieval under a generative prior. In: Bengio S, Wallach H, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R, editors. Advances in neural information processing systems (NeurIPS); Montreal, Canada: The Neural Information Processing Systems Foundation; 2018, p. 9136–9146.
  • Zhang G, Guan T, Shen Z, et al. Fast phase retrieval in off-axis digital holographic microscopy through deep learning. Opt Express. 2018;26:19388–19405.
  • Yuan X, Pu Y. Parallel lensless compressive imaging via deep convolutional neural networks. Opt Express. 2018;26:1961–1977.
  • Nguyen T, Bui V, Lam V, et al. Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection. Opt Express. 2017;25:15043–15057.
  • Wu Y, Rivenson Y, Zhang Y, et al. Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery. Optica. 2018;5:704–710.
  • Rivenson Y, Zhang Y, Gunaydin H, et al. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light Sci Appl. 2018;7:17141–17149.
  • Wang H, Lyu M, Situ G. eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction. Opt Express. 2018;26:22603–22614.
  • Ren Z, Xu Z, Lam EY. Learning-based nonparametric autofocusing for digital holography. Optica. 2018;5:337–344.
  • Pitkäaho T, Manninen A, Naughton T. Focus prediction in digital holographic microscopy using deep convolutional neural networks. J Appl Opt. 2019;58:A202–A208.
  • Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. [arXiv preprint arXiv:1502.03167, 2015].
  • Kingma DP, Ba J. Adam: A method for stochastic optimization. http://arxiv.org/abs/1412.6980; 2017.
  • Wang Z, Bovik AC, Sheikh HR, et al. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13:600–612.
  • Hariharan P. Phase-shifting interferometry: minimization of systematic errors. Opt Eng. 2000;39:967–969.
  • Tsinopoulos SV, Polyzos D. Scattering of He-Ne laser light by an average-sized red blood cell. Appl Opt. 1998;38:5499–5510.
  • Wang Z, Marks DL, Carney PS, et al. Spatial light interference tomography. Opt Express. 2011;19:19907–19918.

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