Abstract

We propose a new learning-based approach for 3D particle field imaging using holography. Our approach uses a U-net architecture incorporating residual connections, Swish activation, hologram preprocessing, and transfer learning to cope with challenges arising in particle holograms where accurate measurement of individual particles is crucial. Assessments on both synthetic and experimental holograms demonstrate a significant improvement in particle extraction rate, localization accuracy and speed compared to prior methods over a wide range of particle concentrations, including highly dense concentrations where other methods are unsuitable. Our approach can be potentially extended to other types of computational imaging tasks with similar features.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2019 (10)

S. Shao, C. Li, and J. Hong, “A hybrid image processing method for measuring 3D bubble distribution using digital inline holography,” Chem. Eng. Sci. 207, 929–941 (2019).
[Crossref]

A. Berdeu, O. Flasseur, L. Méès, L. Denis, F. Momey, T. Olivier, N. Grosjean, and C. Fournier, “Reconstruction of in-line holograms: combining model-based and regularized inversion,” Opt. Express 27(10), 14951 (2019).
[Crossref]

K. Mallery and J. Hong, “Regularized inverse holographic volume reconstruction for 3D particle tracking,” Opt. Express 27(13), 18069 (2019).
[Crossref]

G. Barbastathis, A. Ozcan, and G. Situ, “On the use of deep learning for computational imaging,” Optica 6(8), 921–943 (2019).
[Crossref]

T. Liu, K. D. Haan, Y. Riverson, Z. Wei, X. Zeng, Y. Zhang, and A. Ozcan, “Deep learning-based super-resolution in coherent image systems,” Sci. Rep. 9(1), 3926 (2019).
[Crossref]

T. Liu, Z. Wei, Y. Riverson, K. Haan, Y. Zhang, Y. Wu, and A. Ozcan, “Deep learning-based color holographic microscopy,” J. Biophotonics 12(11), e201900107 (2019).
[Crossref]

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), 4765–4768 (2019).
[Crossref]

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), 4276–4289 (2019).
[Crossref]

T. Shimobaba, T. Takahashi, Y. Yamamoto, Y. Endo, A. Shiraki, T. Nishitsuji, N. Hoshikawa, T. Kakue, and T. Ito, “Digital holographic particle volume reconstruction using a deep neural network,” Appl. Opt. 58(8), 1900–1906 (2019).
[Crossref]

Y. Fu and Y. Liu, “BubGan: Bubble generative adversarial networks for synthesizing realistic bubbly flow images,” Chem. Eng. Sci. 204, 35–47 (2019).
[Crossref]

2018 (8)

Z. Gürücs, M. Tamanitsu, V. Bianco, P. Wolf, S. Roy, K. Shindo, K. Yanny, Y. Wu, H. C. Koydemir, Y. Riverson, and A. Ozcan, “A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples,” Light: Sci. Appl. 7(1), 66 (2018).
[Crossref]

Z. Ren, Z. Xu, and E. Y. Lam, “Learning-based nonparametric autofocusing for digital holography,” Optica 5(4), 337–344 (2018).
[Crossref]

M. D. Hannel, A. Abdulali, M. O’Brien, and D. G. Grier, “Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles,” Opt. Express 26(12), 15221–15231 (2018).
[Crossref]

Y. Riverson, Y. Zhang, H. Günaydin, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light: Sci. Appl. 7(2), 17141 (2018).
[Crossref]

Y. Wu, Y. Riverson, 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), 704–710 (2018).
[Crossref]

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), 22603–22614 (2018).
[Crossref]

F. Jolivet, F. Momey, L. Denis, L. Méès, N. Faure, N. Grosjean, F. Pinston, J. L. Marié, and C. Fournier, “Regularized reconstruction of absorbing and phase objects from a single in-line hologram, application to fluid mechanics and micro-biology,” Opt. Express 26(7), 8923 (2018).
[Crossref]

J. Gao and J. Katz, “Self-calibrated microscopic dual-view tomographic holography for 3D flow measurements,” Opt. Express 26(13), 16708–16725 (2018).
[Crossref]

2017 (4)

B. Mandracchia, V. Bianco, Z. Wang, M. Mugnano, A. Bramanti, M. Paturzo, and P. Ferraro, “Holographic microscope slide in a spatio-temporal imaging modality for reliable 3D cell counting,” Lab Chip 17(16), 2831–2838 (2017).
[Crossref]

J. K. Adams, V. Boominathan, B. W. Avants, D. G. Vercosa, F. Ye, R. G. Baraniuk, J. T. Robinson, and A. Veeraraghavan, “Single frame fluorescence microscopy with ultraminiature lensless flatscope,” Sci. Adv. 3(12), e1701548 (2017).
[Crossref]

E. M. Hall, D. R. Guildenbecher, and B. S. Thurow, “Uncertainty characterization of particle location from refocused plenoptic images,” Opt. Express 25(18), 21801–21814 (2017).
[Crossref]

M. Lyu, W. Wang, H. Wang, H. Wang, G. Li, N. Chen, and G. Situ, “Deep-learning-based ghost imaging,” Sci. Rep. 7(1), 17865 (2017).
[Crossref]

2016 (3)

S. S. Kumar, Y. Sun, S. Zou, and J. Hong, “3D Holographic Observatory for Long-term Monitoring of Complex Behaviors in Drosophila,” Sci. Rep. 6(1), 33001 (2016).
[Crossref]

C. A. Lindensmith, S. Rider, M. Bedrossian, J. K. Wallace, E. Serabyn, G. M. Showalter, J. W. Deming, and J. L. Nadeau, “A submersible, off-axis holographic microscope for detection of microbial motility and morphology in aqueous and icy environments,” PLoS One 11(1), e0147700 (2016).
[Crossref]

N. Verrier, N. Grosjean, E. Dib, L. Méès, C. Fournier, and J. L. Marié, “Improvement of the size estimation of 3D tracked droplets using digital in-line holography with joint estimation reconstruction,” Meas. Sci. Technol. 27(4), 045001 (2016).
[Crossref]

2015 (4)

M. Toloui and J. Hong, “High fidelity digital inline holographic method for 3D flow measurements,” Opt. Express 23(21), 27159 (2015).
[Crossref]

M. J. Beals, J. P. Fugal, R. A. Shaw, J. Lu, S. M. Spuler, and J. L. Stith, “Holographic measurements of inhomogeneous cloud mixing at the centimeter scale,” Science 350(6256), 87–90 (2015).
[Crossref]

K. M. Taute, S. Gude, S. J. Tans, and T. S. Shimizu, “High-throughput 3D tracking of bacteria on a standard phase contrast microscope,” Nat. Commun. 6(1), 8776 (2015).
[Crossref]

T. Latychevskaia and H. W. Fink, “Practical algorithms for simulation and reconstruction of digital in-line holograms,” Appl. Opt. 54(9), 2424–2434 (2015).
[Crossref]

2014 (4)

P. M. S. Roma, L. Siman, F. T. Amaral, U. Agero, and O. N. Mequitam, “Total three-dimensional imaging of phase objects using defocusing microscopy: application to red blood cells,” Appl. Phys. Lett. 104(25), 251107 (2014).
[Crossref]

R. Prevedel, Y. G. Yoon, M. Hoffmann, N. Pak, G. Wetzstein, S. Kato, T. Schrödel, R. Raskar, M. Zimmer, E. S. Boyden, and A. Vaziri, “Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy,’,” Nat. Methods 11(7), 727–730 (2014).
[Crossref]

A. Wang, Q. Marashdeh, and L. S. Fan, “ECVT imaging of 3D spiral bubble plume structures in gas-liquid bubble columns,” Can. J. Chem. Eng. 92(12), 2078–2087 (2014).
[Crossref]

T. Latychevskaia and H. W. Fink, “Holographic time-resolved particle tracking by means of three-dimensional volumetric deconvolution,” Opt. Express 22(17), 20994 (2014).
[Crossref]

2013 (2)

D. R. Guildenbecher, J. Gao, P. L. Reu, and J. Chen, “Digital holography simulations and experiments to quantify the accuracy of 3D particle location and 2D sizing using a proposed hybrid method,” Appl. Opt. 52(16), 3790–3801 (2013).
[Crossref]

M. T. Ekvall, G. Bianco, S. Linse, H. Linke, J. Bäckman, and L. A. Hansson, “Three-dimensional tracking of small aquatic organisms using fluorescent nanoparticles,” PLoS One 8(11), e78498 (2013).
[Crossref]

2012 (2)

T.-W. Su, L. Xue, and A. Ozcan, “High-throughput lensfree 3D tracking of human sperms reveals rare statistics of helical trajectories,” Proc. Natl. Acad. Sci. U. S. A. 109(40), 16018–16022 (2012).
[Crossref]

S. Talapatra, J. Sullivan, J. Katz, M. Twardowski, H. Czerski, P. Donaghay, J. Hong, J. Rines, M. McFarland, A. R. Nayak, and C. Zhang, “Application of in-situ digital holography in the study of particles, organisms and bubbles within their natural environment,” Proc. SPIE 8372, 837205 (2012).
[Crossref]

2010 (2)

2009 (4)

J. Sheng, E. Malkiel, and J. Katz, “Buffer layer structures associated with extreme wall stress events in a smooth wall turbulent boundary layer,” J. Fluid Mech. 633, 17–60 (2009).
[Crossref]

J. Yu, C. Wum S, P. Sahu, L. P. Fernando, C. Szymanski, and J. McNeill, “Nanoscale 3D tracking with conjugated polymer nanoparticles,” J. Am. Chem. Soc. 131(51), 18410–18414 (2009).
[Crossref]

K. J. Batenburg, S. Bals, J. Sijbers, C. Kübel, P. A. Midgley, J. C. Hernandez, U. Kaiser, E. R. Encina, E. A. Coranado, and G. Van Tenedeloo, “3D imaging of nanomaterials by discrete tomography,” Ultramicroscopy 109(6), 730–740 (2009).
[Crossref]

Y. S. Choi and S. J. Lee, “Three-dimensional volumetric measurement of red blood cell motion using digital inline holography,” Appl. Opt. 48(16), 2983–2990 (2009).
[Crossref]

2007 (3)

F. Soulez, L. Denis, E. Thiébaut, C. Fournier, and C. Goepfert, “Inverse problem approach in particle digital holography: out-of-field particle detection made possible,” J. Opt. Soc. Am. A 24(12), 3708–3716 (2007).
[Crossref]

F. Pereira, J. Lu, E. Castano-Graff, and M. Gharib, “Microscale 3D flow mapping with µDDPIV,” Exp. Fluids 42(4), 589–599 (2007).
[Crossref]

T. S. Ralston, D. L. Marks, P. S. Carney, and S. A. Boppart, “Interferometric synthetic aperture microscopy,” Nat. Phys. 3(2), 129–134 (2007).
[Crossref]

2004 (1)

2002 (1)

H. Sun, H. Dong, M. A. Player, J. Watson, D. M. Paterson, and R. Perkins, “In-line digital video holography for the study of erosion processes in sediments,” Meas. Sci. Technol. 13(10), L7–L12 (2002).
[Crossref]

1999 (2)

E. Malkiel, O. Alquaddomi, and J. Katz, “Measurements of plankton distribution in the ocean using submersible holography,” Meas. Sci. Technol. 10(12), 1142–1152 (1999).
[Crossref]

V. Kebbel, M. Adams, H.-J. Hartmann, and W. Jüptner, “Digital holography as a versatile optical diagnostic method for microgravity experiments,” Meas. Sci. Technol. 10(10), 893–899 (1999).
[Crossref]

1964 (1)

J. P. Huber, “Robust estimation of a location parameter,” Ann. Math. Stat. 35(1), 73–101 (1964).
[Crossref]

Abdulali, A.

Abdulkadir, A.

Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-net: learning dense volumetric segmentation from sparse annotation,” in Med. Image Comput. Comput. Assist. Interv. 2016-Germany, (Springer, 2016), pp. 424–432.

Acuna, D.

J. Tremblay, P. Prakash, D. Acuna, M. Brophy, V. Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon, and S. Birchfield, “Training deep networks with synthetic data: bridging the reality gap by domain randomization,” in Proc. of the IEEE CVPR 2018-Salt Lake City, (IEEE, 2018), pp. 969–977.

Adam, H.

L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proc. of the IEEE CVPR 2018-Salt Lake City, (IEEE, 2018), pp. 801–818.

Adams, J. K.

J. K. Adams, V. Boominathan, B. W. Avants, D. G. Vercosa, F. Ye, R. G. Baraniuk, J. T. Robinson, and A. Veeraraghavan, “Single frame fluorescence microscopy with ultraminiature lensless flatscope,” Sci. Adv. 3(12), e1701548 (2017).
[Crossref]

Adams, M.

V. Kebbel, M. Adams, H.-J. Hartmann, and W. Jüptner, “Digital holography as a versatile optical diagnostic method for microgravity experiments,” Meas. Sci. Technol. 10(10), 893–899 (1999).
[Crossref]

Agero, U.

P. M. S. Roma, L. Siman, F. T. Amaral, U. Agero, and O. N. Mequitam, “Total three-dimensional imaging of phase objects using defocusing microscopy: application to red blood cells,” Appl. Phys. Lett. 104(25), 251107 (2014).
[Crossref]

Allano, D.

Alquaddomi, O.

E. Malkiel, O. Alquaddomi, and J. Katz, “Measurements of plankton distribution in the ocean using submersible holography,” Meas. Sci. Technol. 10(12), 1142–1152 (1999).
[Crossref]

Amaral, F. T.

P. M. S. Roma, L. Siman, F. T. Amaral, U. Agero, and O. N. Mequitam, “Total three-dimensional imaging of phase objects using defocusing microscopy: application to red blood cells,” Appl. Phys. Lett. 104(25), 251107 (2014).
[Crossref]

Anil, C.

J. Tremblay, P. Prakash, D. Acuna, M. Brophy, V. Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon, and S. Birchfield, “Training deep networks with synthetic data: bridging the reality gap by domain randomization,” in Proc. of the IEEE CVPR 2018-Salt Lake City, (IEEE, 2018), pp. 969–977.

Avants, B. W.

J. K. Adams, V. Boominathan, B. W. Avants, D. G. Vercosa, F. Ye, R. G. Baraniuk, J. T. Robinson, and A. Veeraraghavan, “Single frame fluorescence microscopy with ultraminiature lensless flatscope,” Sci. Adv. 3(12), e1701548 (2017).
[Crossref]

Ba, J.

D. P. Kingma and J. Ba, “Adam: a method for stochastic optimization,” axXiv: 1412.6980 (2014).

Bäckman, J.

M. T. Ekvall, G. Bianco, S. Linse, H. Linke, J. Bäckman, and L. A. Hansson, “Three-dimensional tracking of small aquatic organisms using fluorescent nanoparticles,” PLoS One 8(11), e78498 (2013).
[Crossref]

Bals, S.

K. J. Batenburg, S. Bals, J. Sijbers, C. Kübel, P. A. Midgley, J. C. Hernandez, U. Kaiser, E. R. Encina, E. A. Coranado, and G. Van Tenedeloo, “3D imaging of nanomaterials by discrete tomography,” Ultramicroscopy 109(6), 730–740 (2009).
[Crossref]

Baraniuk, R. G.

J. K. Adams, V. Boominathan, B. W. Avants, D. G. Vercosa, F. Ye, R. G. Baraniuk, J. T. Robinson, and A. Veeraraghavan, “Single frame fluorescence microscopy with ultraminiature lensless flatscope,” Sci. Adv. 3(12), e1701548 (2017).
[Crossref]

Barbastathis, G.

Batenburg, K. J.

K. J. Batenburg, S. Bals, J. Sijbers, C. Kübel, P. A. Midgley, J. C. Hernandez, U. Kaiser, E. R. Encina, E. A. Coranado, and G. Van Tenedeloo, “3D imaging of nanomaterials by discrete tomography,” Ultramicroscopy 109(6), 730–740 (2009).
[Crossref]

Beals, M. J.

M. J. Beals, J. P. Fugal, R. A. Shaw, J. Lu, S. M. Spuler, and J. L. Stith, “Holographic measurements of inhomogeneous cloud mixing at the centimeter scale,” Science 350(6256), 87–90 (2015).
[Crossref]

Bedrossian, M.

C. A. Lindensmith, S. Rider, M. Bedrossian, J. K. Wallace, E. Serabyn, G. M. Showalter, J. W. Deming, and J. L. Nadeau, “A submersible, off-axis holographic microscope for detection of microbial motility and morphology in aqueous and icy environments,” PLoS One 11(1), e0147700 (2016).
[Crossref]

Bengio, Y.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning (MIT Press, 2016).

Berdeu, A.

Bianco, G.

M. T. Ekvall, G. Bianco, S. Linse, H. Linke, J. Bäckman, and L. A. Hansson, “Three-dimensional tracking of small aquatic organisms using fluorescent nanoparticles,” PLoS One 8(11), e78498 (2013).
[Crossref]

Bianco, V.

Z. Gürücs, M. Tamanitsu, V. Bianco, P. Wolf, S. Roy, K. Shindo, K. Yanny, Y. Wu, H. C. Koydemir, Y. Riverson, and A. Ozcan, “A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples,” Light: Sci. Appl. 7(1), 66 (2018).
[Crossref]

B. Mandracchia, V. Bianco, Z. Wang, M. Mugnano, A. Bramanti, M. Paturzo, and P. Ferraro, “Holographic microscope slide in a spatio-temporal imaging modality for reliable 3D cell counting,” Lab Chip 17(16), 2831–2838 (2017).
[Crossref]

V. Bianco, P. Memmolo, F. Merola, P. Carcagni, C. Distante, and P. Ferraro, “High-accuracy identification of micro-plastics by holographic microscopy enabled support vector machine,” in Quantitative Phase Imaging V, (SPIE, 2019), pp. 108870F-1–108870F-7.

Birchfield, S.

J. Tremblay, P. Prakash, D. Acuna, M. Brophy, V. Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon, and S. Birchfield, “Training deep networks with synthetic data: bridging the reality gap by domain randomization,” in Proc. of the IEEE CVPR 2018-Salt Lake City, (IEEE, 2018), pp. 969–977.

Boochoon, S.

J. Tremblay, P. Prakash, D. Acuna, M. Brophy, V. Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon, and S. Birchfield, “Training deep networks with synthetic data: bridging the reality gap by domain randomization,” in Proc. of the IEEE CVPR 2018-Salt Lake City, (IEEE, 2018), pp. 969–977.

Boominathan, V.

J. K. Adams, V. Boominathan, B. W. Avants, D. G. Vercosa, F. Ye, R. G. Baraniuk, J. T. Robinson, and A. Veeraraghavan, “Single frame fluorescence microscopy with ultraminiature lensless flatscope,” Sci. Adv. 3(12), e1701548 (2017).
[Crossref]

Boppart, S. A.

T. S. Ralston, D. L. Marks, P. S. Carney, and S. A. Boppart, “Interferometric synthetic aperture microscopy,” Nat. Phys. 3(2), 129–134 (2007).
[Crossref]

Boyden, E. S.

R. Prevedel, Y. G. Yoon, M. Hoffmann, N. Pak, G. Wetzstein, S. Kato, T. Schrödel, R. Raskar, M. Zimmer, E. S. Boyden, and A. Vaziri, “Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy,’,” Nat. Methods 11(7), 727–730 (2014).
[Crossref]

Bramanti, A.

B. Mandracchia, V. Bianco, Z. Wang, M. Mugnano, A. Bramanti, M. Paturzo, and P. Ferraro, “Holographic microscope slide in a spatio-temporal imaging modality for reliable 3D cell counting,” Lab Chip 17(16), 2831–2838 (2017).
[Crossref]

Brophy, M.

J. Tremblay, P. Prakash, D. Acuna, M. Brophy, V. Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon, and S. Birchfield, “Training deep networks with synthetic data: bridging the reality gap by domain randomization,” in Proc. of the IEEE CVPR 2018-Salt Lake City, (IEEE, 2018), pp. 969–977.

Brox, T.

Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-net: learning dense volumetric segmentation from sparse annotation,” in Med. Image Comput. Comput. Assist. Interv. 2016-Germany, (Springer, 2016), pp. 424–432.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: convolutional networks for biomedical image segmentation,” in Med. Image Comput. Comput. Assist. Interv. 2015-Germany, (Springer, 2015), pp. 234–241.

Cameracci, E.

J. Tremblay, P. Prakash, D. Acuna, M. Brophy, V. Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon, and S. Birchfield, “Training deep networks with synthetic data: bridging the reality gap by domain randomization,” in Proc. of the IEEE CVPR 2018-Salt Lake City, (IEEE, 2018), pp. 969–977.

Carcagni, P.

V. Bianco, P. Memmolo, F. Merola, P. Carcagni, C. Distante, and P. Ferraro, “High-accuracy identification of micro-plastics by holographic microscopy enabled support vector machine,” in Quantitative Phase Imaging V, (SPIE, 2019), pp. 108870F-1–108870F-7.

Carney, P. S.

T. S. Ralston, D. L. Marks, P. S. Carney, and S. A. Boppart, “Interferometric synthetic aperture microscopy,” Nat. Phys. 3(2), 129–134 (2007).
[Crossref]

Castano-Graff, E.

F. Pereira, J. Lu, E. Castano-Graff, and M. Gharib, “Microscale 3D flow mapping with µDDPIV,” Exp. Fluids 42(4), 589–599 (2007).
[Crossref]

Chen, J.

Chen, L. C.

L. C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proc. of the IEEE CVPR 2018-Salt Lake City, (IEEE, 2018), pp. 801–818.

Chen, N.

M. Lyu, W. Wang, H. Wang, H. Wang, G. Li, N. Chen, and G. Situ, “Deep-learning-based ghost imaging,” Sci. Rep. 7(1), 17865 (2017).
[Crossref]

Choi, Y. S.

Chollet, F.

F. Chollet, keras. GitHub repository (2015), https://github.com/fchollet/keras .

Çiçek, Ö.

Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D U-net: learning dense volumetric segmentation from sparse annotation,” in Med. Image Comput. Comput. Assist. Interv. 2016-Germany, (Springer, 2016), pp. 424–432.

Coëtmellec, S.

Coranado, E. A.

K. J. Batenburg, S. Bals, J. Sijbers, C. Kübel, P. A. Midgley, J. C. Hernandez, U. Kaiser, E. R. Encina, E. A. Coranado, and G. Van Tenedeloo, “3D imaging of nanomaterials by discrete tomography,” Ultramicroscopy 109(6), 730–740 (2009).
[Crossref]

Courville, A.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning (MIT Press, 2016).

Czerski, H.

S. Talapatra, J. Sullivan, J. Katz, M. Twardowski, H. Czerski, P. Donaghay, J. Hong, J. Rines, M. McFarland, A. R. Nayak, and C. Zhang, “Application of in-situ digital holography in the study of particles, organisms and bubbles within their natural environment,” Proc. SPIE 8372, 837205 (2012).
[Crossref]

Deming, J. W.

C. A. Lindensmith, S. Rider, M. Bedrossian, J. K. Wallace, E. Serabyn, G. M. Showalter, J. W. Deming, and J. L. Nadeau, “A submersible, off-axis holographic microscope for detection of microbial motility and morphology in aqueous and icy environments,” PLoS One 11(1), e0147700 (2016).
[Crossref]

Denis, L.

Di, J.

Dib, E.

N. Verrier, N. Grosjean, E. Dib, L. Méès, C. Fournier, and J. L. Marié, “Improvement of the size estimation of 3D tracked droplets using digital in-line holography with joint estimation reconstruction,” Meas. Sci. Technol. 27(4), 045001 (2016).
[Crossref]

Ding, C.

C. Ding, Z. Ding, X. He, and H. Zha, “R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization,” in Proc. of 23rd ICML 2006-Pittsburg, (ACM, 2006), pp. 281–288.

Ding, Z.

C. Ding, Z. Ding, X. He, and H. Zha, “R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization,” in Proc. of 23rd ICML 2006-Pittsburg, (ACM, 2006), pp. 281–288.

Distante, C.

V. Bianco, P. Memmolo, F. Merola, P. Carcagni, C. Distante, and P. Ferraro, “High-accuracy identification of micro-plastics by holographic microscopy enabled support vector machine,” in Quantitative Phase Imaging V, (SPIE, 2019), pp. 108870F-1–108870F-7.

Domínguez-Caballero, J. A.

Donaghay, P.

S. Talapatra, J. Sullivan, J. Katz, M. Twardowski, H. Czerski, P. Donaghay, J. Hong, J. Rines, M. McFarland, A. R. Nayak, and C. Zhang, “Application of in-situ digital holography in the study of particles, organisms and bubbles within their natural environment,” Proc. SPIE 8372, 837205 (2012).
[Crossref]

Dong, H.

H. Sun, H. Dong, M. A. Player, J. Watson, D. M. Paterson, and R. Perkins, “In-line digital video holography for the study of erosion processes in sediments,” Meas. Sci. Technol. 13(10), L7–L12 (2002).
[Crossref]

Dou, J.

Ekvall, M. T.

M. T. Ekvall, G. Bianco, S. Linse, H. Linke, J. Bäckman, and L. A. Hansson, “Three-dimensional tracking of small aquatic organisms using fluorescent nanoparticles,” PLoS One 8(11), e78498 (2013).
[Crossref]

Elfros, A. A.

P. Isola, J. Y. Zhu, T. Zhou, and A. A. Elfros, “Image-to-image translation with conditional adversarial networks,” in Proc. of the IEEE CVPR 2017-Honolulu, (IEEE, 2017), pp. 1125–1134.

Encina, E. R.

K. J. Batenburg, S. Bals, J. Sijbers, C. Kübel, P. A. Midgley, J. C. Hernandez, U. Kaiser, E. R. Encina, E. A. Coranado, and G. Van Tenedeloo, “3D imaging of nanomaterials by discrete tomography,” Ultramicroscopy 109(6), 730–740 (2009).
[Crossref]

Endo, Y.

Fan, L. S.

A. Wang, Q. Marashdeh, and L. S. Fan, “ECVT imaging of 3D spiral bubble plume structures in gas-liquid bubble columns,” Can. J. Chem. Eng. 92(12), 2078–2087 (2014).
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Faure, N.

Fernando, L. P.

J. Yu, C. Wum S, P. Sahu, L. P. Fernando, C. Szymanski, and J. McNeill, “Nanoscale 3D tracking with conjugated polymer nanoparticles,” J. Am. Chem. Soc. 131(51), 18410–18414 (2009).
[Crossref]

Ferraro, P.

B. Mandracchia, V. Bianco, Z. Wang, M. Mugnano, A. Bramanti, M. Paturzo, and P. Ferraro, “Holographic microscope slide in a spatio-temporal imaging modality for reliable 3D cell counting,” Lab Chip 17(16), 2831–2838 (2017).
[Crossref]

V. Bianco, P. Memmolo, F. Merola, P. Carcagni, C. Distante, and P. Ferraro, “High-accuracy identification of micro-plastics by holographic microscopy enabled support vector machine,” in Quantitative Phase Imaging V, (SPIE, 2019), pp. 108870F-1–108870F-7.

Fink, H. W.

Fischer, P.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: convolutional networks for biomedical image segmentation,” in Med. Image Comput. Comput. Assist. Interv. 2015-Germany, (Springer, 2015), pp. 234–241.

Flasseur, O.

Fournier, C.

Fu, Y.

Y. Fu and Y. Liu, “BubGan: Bubble generative adversarial networks for synthesizing realistic bubbly flow images,” Chem. Eng. Sci. 204, 35–47 (2019).
[Crossref]

Fugal, J. P.

M. J. Beals, J. P. Fugal, R. A. Shaw, J. Lu, S. M. Spuler, and J. L. Stith, “Holographic measurements of inhomogeneous cloud mixing at the centimeter scale,” Science 350(6256), 87–90 (2015).
[Crossref]

Gao, J.

Gharib, M.

F. Pereira, J. Lu, E. Castano-Graff, and M. Gharib, “Microscale 3D flow mapping with µDDPIV,” Exp. Fluids 42(4), 589–599 (2007).
[Crossref]

Goepfert, C.

Goodfellow, I.

I. Goodfellow, Y. Bengio, and A. Courville, Deep learning (MIT Press, 2016).

Grier, D. G.

Grosjean, N.

Gude, S.

K. M. Taute, S. Gude, S. J. Tans, and T. S. Shimizu, “High-throughput 3D tracking of bacteria on a standard phase contrast microscope,” Nat. Commun. 6(1), 8776 (2015).
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Guildenbecher, D. R.

Günaydin, H.

Y. Riverson, Y. Zhang, H. Günaydin, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light: Sci. Appl. 7(2), 17141 (2018).
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Y. Wu, Y. Riverson, 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), 704–710 (2018).
[Crossref]

Gürücs, Z.

Z. Gürücs, M. Tamanitsu, V. Bianco, P. Wolf, S. Roy, K. Shindo, K. Yanny, Y. Wu, H. C. Koydemir, Y. Riverson, and A. Ozcan, “A deep learning-enabled portable imaging flow cytometer for cost-effective, high-throughput, and label-free analysis of natural water samples,” Light: Sci. Appl. 7(1), 66 (2018).
[Crossref]

Haan, K.

T. Liu, Z. Wei, Y. Riverson, K. Haan, Y. Zhang, Y. Wu, and A. Ozcan, “Deep learning-based color holographic microscopy,” J. Biophotonics 12(11), e201900107 (2019).
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Haan, K. D.

T. Liu, K. D. Haan, Y. Riverson, Z. Wei, X. Zeng, Y. Zhang, and A. Ozcan, “Deep learning-based super-resolution in coherent image systems,” Sci. Rep. 9(1), 3926 (2019).
[Crossref]

Hall, E. M.

Hannel, M. D.

Hansson, L. A.

M. T. Ekvall, G. Bianco, S. Linse, H. Linke, J. Bäckman, and L. A. Hansson, “Three-dimensional tracking of small aquatic organisms using fluorescent nanoparticles,” PLoS One 8(11), e78498 (2013).
[Crossref]

Hartmann, H.-J.

V. Kebbel, M. Adams, H.-J. Hartmann, and W. Jüptner, “Digital holography as a versatile optical diagnostic method for microgravity experiments,” Meas. Sci. Technol. 10(10), 893–899 (1999).
[Crossref]

He, K.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. of the IEEE CVPR 2016-Las Vegas, (IEEE, 2016), pp. 770–778.

He, X.

C. Ding, Z. Ding, X. He, and H. Zha, “R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization,” in Proc. of 23rd ICML 2006-Pittsburg, (ACM, 2006), pp. 281–288.

Hernandez, J. C.

K. J. Batenburg, S. Bals, J. Sijbers, C. Kübel, P. A. Midgley, J. C. Hernandez, U. Kaiser, E. R. Encina, E. A. Coranado, and G. Van Tenedeloo, “3D imaging of nanomaterials by discrete tomography,” Ultramicroscopy 109(6), 730–740 (2009).
[Crossref]

Hoffmann, M.

R. Prevedel, Y. G. Yoon, M. Hoffmann, N. Pak, G. Wetzstein, S. Kato, T. Schrödel, R. Raskar, M. Zimmer, E. S. Boyden, and A. Vaziri, “Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy,’,” Nat. Methods 11(7), 727–730 (2014).
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Hong, J.

S. Shao, C. Li, and J. Hong, “A hybrid image processing method for measuring 3D bubble distribution using digital inline holography,” Chem. Eng. Sci. 207, 929–941 (2019).
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K. Mallery and J. Hong, “Regularized inverse holographic volume reconstruction for 3D particle tracking,” Opt. Express 27(13), 18069 (2019).
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S. S. Kumar, Y. Sun, S. Zou, and J. Hong, “3D Holographic Observatory for Long-term Monitoring of Complex Behaviors in Drosophila,” Sci. Rep. 6(1), 33001 (2016).
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M. Toloui and J. Hong, “High fidelity digital inline holographic method for 3D flow measurements,” Opt. Express 23(21), 27159 (2015).
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S. Talapatra, J. Sullivan, J. Katz, M. Twardowski, H. Czerski, P. Donaghay, J. Hong, J. Rines, M. McFarland, A. R. Nayak, and C. Zhang, “Application of in-situ digital holography in the study of particles, organisms and bubbles within their natural environment,” Proc. SPIE 8372, 837205 (2012).
[Crossref]

Hoshikawa, N.

Huber, J. P.

J. P. Huber, “Robust estimation of a location parameter,” Ann. Math. Stat. 35(1), 73–101 (1964).
[Crossref]

Hwang, S. H.

Isola, P.

P. Isola, J. Y. Zhu, T. Zhou, and A. A. Elfros, “Image-to-image translation with conditional adversarial networks,” in Proc. of the IEEE CVPR 2017-Honolulu, (IEEE, 2017), pp. 1125–1134.

Ito, T.

Jaferzadeh, K.

Jampani, V.

J. Tremblay, P. Prakash, D. Acuna, M. Brophy, V. Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon, and S. Birchfield, “Training deep networks with synthetic data: bridging the reality gap by domain randomization,” in Proc. of the IEEE CVPR 2018-Salt Lake City, (IEEE, 2018), pp. 969–977.

Javidi, B.

Jolivet, F.

Jüptner, W.

V. Kebbel, M. Adams, H.-J. Hartmann, and W. Jüptner, “Digital holography as a versatile optical diagnostic method for microgravity experiments,” Meas. Sci. Technol. 10(10), 893–899 (1999).
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Kähler, C. J.

M. Raffel, C. E. Willert, F. Scarano, C. J. Kähler, S. T. Wereley, and J. Kompenhans, Particle image velocimetry: a practical guide. (Springer, 2018).

Kaiser, U.

K. J. Batenburg, S. Bals, J. Sijbers, C. Kübel, P. A. Midgley, J. C. Hernandez, U. Kaiser, E. R. Encina, E. A. Coranado, and G. Van Tenedeloo, “3D imaging of nanomaterials by discrete tomography,” Ultramicroscopy 109(6), 730–740 (2009).
[Crossref]

Kakue, T.

Kato, S.

R. Prevedel, Y. G. Yoon, M. Hoffmann, N. Pak, G. Wetzstein, S. Kato, T. Schrödel, R. Raskar, M. Zimmer, E. S. Boyden, and A. Vaziri, “Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy,’,” Nat. Methods 11(7), 727–730 (2014).
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Katz, J.

J. Gao and J. Katz, “Self-calibrated microscopic dual-view tomographic holography for 3D flow measurements,” Opt. Express 26(13), 16708–16725 (2018).
[Crossref]

S. Talapatra, J. Sullivan, J. Katz, M. Twardowski, H. Czerski, P. Donaghay, J. Hong, J. Rines, M. McFarland, A. R. Nayak, and C. Zhang, “Application of in-situ digital holography in the study of particles, organisms and bubbles within their natural environment,” Proc. SPIE 8372, 837205 (2012).
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J. Katz and J. Sheng, “Applications of holography in fluid mechanics and particle dynamics,” Annu. Rev. Fluid Mech. 42(1), 531–555 (2010).
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J. Sheng, E. Malkiel, and J. Katz, “Buffer layer structures associated with extreme wall stress events in a smooth wall turbulent boundary layer,” J. Fluid Mech. 633, 17–60 (2009).
[Crossref]

E. Malkiel, O. Alquaddomi, and J. Katz, “Measurements of plankton distribution in the ocean using submersible holography,” Meas. Sci. Technol. 10(12), 1142–1152 (1999).
[Crossref]

Kebbel, V.

V. Kebbel, M. Adams, H.-J. Hartmann, and W. Jüptner, “Digital holography as a versatile optical diagnostic method for microgravity experiments,” Meas. Sci. Technol. 10(10), 893–899 (1999).
[Crossref]

Kemao, Q.

Kingma, D. P.

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J. Tremblay, P. Prakash, D. Acuna, M. Brophy, V. Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon, and S. Birchfield, “Training deep networks with synthetic data: bridging the reality gap by domain randomization,” in Proc. of the IEEE CVPR 2018-Salt Lake City, (IEEE, 2018), pp. 969–977.

Toloui, M.

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J. Tremblay, P. Prakash, D. Acuna, M. Brophy, V. Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon, and S. Birchfield, “Training deep networks with synthetic data: bridging the reality gap by domain randomization,” in Proc. of the IEEE CVPR 2018-Salt Lake City, (IEEE, 2018), pp. 969–977.

Twardowski, M.

S. Talapatra, J. Sullivan, J. Katz, M. Twardowski, H. Czerski, P. Donaghay, J. Hong, J. Rines, M. McFarland, A. R. Nayak, and C. Zhang, “Application of in-situ digital holography in the study of particles, organisms and bubbles within their natural environment,” Proc. SPIE 8372, 837205 (2012).
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R. Prevedel, Y. G. Yoon, M. Hoffmann, N. Pak, G. Wetzstein, S. Kato, T. Schrödel, R. Raskar, M. Zimmer, E. S. Boyden, and A. Vaziri, “Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy,’,” Nat. Methods 11(7), 727–730 (2014).
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J. K. Adams, V. Boominathan, B. W. Avants, D. G. Vercosa, F. Ye, R. G. Baraniuk, J. T. Robinson, and A. Veeraraghavan, “Single frame fluorescence microscopy with ultraminiature lensless flatscope,” Sci. Adv. 3(12), e1701548 (2017).
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A. Wang, Q. Marashdeh, and L. S. Fan, “ECVT imaging of 3D spiral bubble plume structures in gas-liquid bubble columns,” Can. J. Chem. Eng. 92(12), 2078–2087 (2014).
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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), 22603–22614 (2018).
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T.-W. Su, L. Xue, and A. Ozcan, “High-throughput lensfree 3D tracking of human sperms reveals rare statistics of helical trajectories,” Proc. Natl. Acad. Sci. U. S. A. 109(40), 16018–16022 (2012).
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J. K. Adams, V. Boominathan, B. W. Avants, D. G. Vercosa, F. Ye, R. G. Baraniuk, J. T. Robinson, and A. Veeraraghavan, “Single frame fluorescence microscopy with ultraminiature lensless flatscope,” Sci. Adv. 3(12), e1701548 (2017).
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R. Prevedel, Y. G. Yoon, M. Hoffmann, N. Pak, G. Wetzstein, S. Kato, T. Schrödel, R. Raskar, M. Zimmer, E. S. Boyden, and A. Vaziri, “Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy,’,” Nat. Methods 11(7), 727–730 (2014).
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J. Yu, C. Wum S, P. Sahu, L. P. Fernando, C. Szymanski, and J. McNeill, “Nanoscale 3D tracking with conjugated polymer nanoparticles,” J. Am. Chem. Soc. 131(51), 18410–18414 (2009).
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T. Liu, K. D. Haan, Y. Riverson, Z. Wei, X. Zeng, Y. Zhang, and A. Ozcan, “Deep learning-based super-resolution in coherent image systems,” Sci. Rep. 9(1), 3926 (2019).
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Y. Riverson, Y. Zhang, H. Günaydin, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light: Sci. Appl. 7(2), 17141 (2018).
[Crossref]

Y. Wu, Y. Riverson, 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), 704–710 (2018).
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Figures (7)

Fig. 1.
Fig. 1. The specially-designed U-net architecture for holographic reconstruction of 3D particle field.
Fig. 2.
Fig. 2. A sample training input and training target consisting of 300 particles (i.e., concentration at 0.018 ppp) with a hologram size of 128 ×128 pixels. The hologram is formed with a pixel resolution of 10 µm with a laser illumination wavelength of 632 nm.
Fig. 3.
Fig. 3. Prediction results from the trained model using (a) our U-net architecture and (b) the method presented in Shimobaba et al. [44] (c) and Mallery and Hong [31] for the case of 0.018 ppp (300-particle holograms). The black dots are extracted true particles, red dots are false positives (i.e., unpaired particles from ground truth) and green dots are the false negatives (unpaired particles from the ground truth).
Fig. 4.
Fig. 4. Demonstration of the impact of the proposed model improvements on the training process over the first 200 epochs. (a) Proposed approach, (b) using U-net architecture without residual connection and (c) using mean squared error as loss function. The loss is normalized by its initial value, and each case is randomly initialized 10 times to show the resultant instability of the training for cases (b) and (c). In the image, the green curves correspond to the maximum and minimum normalized loss at each epoch, the blue curves corresponding to each initialization, and the shaded region is the range of loss.
Fig. 5.
Fig. 5. Comparison of prediction results with a 100-particle hologram (a) and a 1000-particle hologram (b). The black dots are extracted true particles, red dots are false positives (i.e., unpaired particles from prediction) and green dots are false negatives (unpaired particles from the ground truth).
Fig. 6.
Fig. 6. (a) Extraction rate under different particle concentrations of the proposed method and compared with the case of Shimobaba et al. [44] and RIHVR [31] and (b) Median position error of extracted particles for the proposed method under different particle concentrations. Note that the dashed lines correspond to the particle concentration of the base model (1.8×10−2 ppp).
Fig. 7.
Fig. 7. (a) A 128×128-pixel enhanced hologram from the experimental data and corresponding volumetric image through the stacking of fluorescent bright field scanning of the same sample for determining the ground truth (b). (c). Prediction results in comparison from the machine learning model. The black dots are extracted true particles, red dots are false positives (i.e., unpaired particles from ground truth) and green dots are the false negatives (unpaired particles from the ground truth.).

Equations (7)

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f ( x ) = x 1 + e x
u p ( x , y , z ) = F 1 [ F ( I ( x , y ) ) × F ( exp ( j k z ) j λ z exp { j k 2 z [ ( x 2 + y 2 ) ] } ) ]
z approx = arg m z a x { u p ( x , y , z ) × conj [ u p ( x , y , z ) ] }
P ( x , y ) = max z { angle [ u p ( x , y , z ) ] }
L = { 1 2 | | Y X | | 2 2       | | Y X | | 1 δ ,   δ | | Y X | | 1 1 2 δ 2        otherwise .
L = ( 1 α ) ( | | Y X | | 2 2 ) + α | | Y | | T V 2
| | Y | | T V = i = 1 N x j = 1 N y ( Y i , j Y i i , j ) 2 + ( Y i , j Y i , j 1 ) 2

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