Abstract

Lens-free holographic microscopy (LFHM) provides a cost-effective tool for large field-of-view imaging in various biomedical applications. However, due to the unit optical magnification, its spatial resolution is limited by the pixel size of the imager. Pixel super-resolution (PSR) technique tackles this problem by using a series of sub-pixel shifted low-resolution (LR) lens-free holograms to form the high-resolution (HR) hologram. Conventional iterative PSR methods require a large number of measurements and a time-consuming reconstruction process, limiting the throughput of LFHM in practice. Here we report a deep learning-based PSR approach to enhance the resolution of LFHM. Compared with the existing PSR methods, our neural network-based approach outputs the HR hologram in an end-to-end fashion and maintains consistency in resolution improvement with a reduced number of LR holograms. Moreover, by exploiting the resolution degradation model in the imaging process, the network can be trained with a data set synthesized from the LR hologram itself without resorting to the HR ground truth. We validated the effectiveness and the robustness of our method by imaging various types of samples using a single network trained on an entirely different data set. This deep learning-based PSR approach can significantly accelerate both the data acquisition and the HR hologram reconstruction processes, therefore providing a practical solution to fast, lens-free, super-resolution imaging.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (6)

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref] [PubMed]

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[Crossref]

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).
[Crossref]

E. Nehme, L. E. Weiss, T. Michaeli, and Y. Shechtman, “Deep-STORM: super-resolution single-molecule microscopy by deep learning,” Optica 5(4), 458–464 (2018).
[Crossref]

J. Zhang, Q. Chen, J. Li, J. Sun, and C. Zuo, “Lensfree dynamic super-resolved phase imaging based on active micro-scanning,” Opt. Lett. 43(15), 3714–3717 (2018).
[Crossref] [PubMed]

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[Crossref] [PubMed]

2017 (7)

K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Trans. Image Process. 26(7), 3142–3155 (2017).
[Crossref] [PubMed]

C. Fournier, F. Jolivet, L. Denis, N. Verrier, E. Thiebaut, C. Allier, and T. Fournel, “Pixel super-resolution in digital holography by regularized reconstruction,” Appl. Opt. 56(1), 69–77 (2017).
[Crossref]

Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4(11), 1437–1443 (2017).
[Crossref]

C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J. M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry A 91(5), 433–442 (2017).
[Crossref] [PubMed]

J. Zhang, J. Sun, Q. Chen, J. Li, and C. Zuo, “Adaptive pixel-super-resolved lensfree in-line digital holography for wide-field on-chip microscopy,” Sci. Rep. 7(1), 11777 (2017).
[Crossref] [PubMed]

B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, “Enhanced deep residual networks for single image super-resolution,” in The IEEE conference on computer vision and pattern recognition (CVPR) workshops, vol.  1, p. 4 (2017).

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” in CVPR,  2(3), p. 4 (2017).

2016 (5)

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref] [PubMed]

J. Song, C. Leon Swisher, H. Im, S. Jeong, D. Pathania, Y. Iwamoto, M. Pivovarov, R. Weissleder, and H. Lee, “Sparsity-based pixel super resolution for lens-free digital in-line holography,” Sci. Rep. 6, 24681 (2016).
[Crossref] [PubMed]

W. Luo, Y. Zhang, Z. Göröcs, A. Feizi, and A. Ozcan, “Propagation phasor approach for holographic image reconstruction,” Sci. Rep. 6(1), 22738 (2016).
[Crossref] [PubMed]

W. Luo, Y. Zhang, A. Feizi, Z. Göröcs, and A. Ozcan, “Pixel super-resolution using wavelength scanning,” Light Sci. Appl. 5(4), e16060 (2016).
[Crossref] [PubMed]

E. McLeod and A. Ozcan, “Unconventional methods of imaging: computational microscopy and compact implementations,” Rep. Prog. Phys. 79(7), 076001 (2016).
[Crossref] [PubMed]

2015 (2)

Y. Huang, W. Wang, and L. Wang, “Bidirectional recurrent convolutional networks for multi-frame super-resolution,” Adv. Neural Inf. Process. Syst. 28235–243 (2015).

W. Luo, A. Greenbaum, Y. Zhang, and A. Ozcan, “Synthetic aperture-based on-chip microscopy,” Light Sci. Appl. 4(3), e261 (2015).
[Crossref]

2014 (1)

R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, and et al., “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).

2013 (1)

A. Greenbaum, W. Luo, B. Khademhosseinieh, T.-W. Su, A. F. Coskun, and A. Ozcan, “Increased space-bandwidth product in pixel super-resolved lensfree on-chip microscopy,” Sci. Rep. 3(1), 1717 (2013).
[Crossref]

2012 (1)

S. Schumacher, J. Nestler, T. Otto, M. Wegener, E. Ehrentreich-Förster, D. Michel, K. Wunderlich, S. Palzer, K. Sohn, A. Weber, M. Burgard, A. Grzesiak, A. Teichert, A. Brandenburg, B. Koger, J. Albers, E. Nebling, and F. F. Bier, “Highly-integrated lab-on-chip system for point-of-care multiparameter analysis,” Lab Chip 12(3), 464–473 (2012).
[Crossref] [PubMed]

2011 (1)

2010 (4)

W. Bishara, T.-W. Su, A. F. Coskun, and A. Ozcan, “Lensfree on-chip microscopy over a wide field-of-view using pixel super-resolution,” Opt. Express 18(11), 11181–11191 (2010).
[Crossref] [PubMed]

W. Bishara, H. Zhu, and A. Ozcan, “Holographic opto-fluidic microscopy,” Opt. Express 18(26), 27499–27510 (2010).
[Crossref] [PubMed]

G. Stybayeva, O. Mudanyali, S. Seo, J. Silangcruz, M. Macal, E. Ramanculov, S. Dandekar, A. Erlinger, A. Ozcan, and A. Revzin, “Lensfree holographic imaging of antibody microarrays for high-throughput detection of leukocyte numbers and function,” Anal. Chem. 82(9), 3736–3744 (2010).
[Crossref] [PubMed]

O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010).
[Crossref] [PubMed]

2009 (2)

S. Seo, T.-W. Su, D. K. Tseng, A. Erlinger, and A. Ozcan, “Lensfree holographic imaging for on-chip cytometry and diagnostics,” Lab Chip 9(6), 777–787 (2009).
[Crossref] [PubMed]

K. Matsushima and T. Shimobaba, “Band-limited angular spectrum method for numerical simulation of free-space propagation in far and near fields,” Opt. Express 17(22), 19662–19673 (2009).
[Crossref] [PubMed]

2008 (2)

2006 (1)

2004 (3)

S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar, “Fast and robust multiframe super resolution,” IEEE Trans. Image Process. 13(10), 1327–1344 (2004).
[Crossref] [PubMed]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

Y. Lu, M. Inamura, and M. del Carmen Valdes, “Super-resolution of the undersampled and subpixel shifted image sequence by a neural network,” Int. J. Imaging Syst. Technol. 14(1), 8–15 (2004).
[Crossref]

2002 (1)

W. T. Freeman, T. R. Jones, and E. C. Pasztor, “Example-based super-resolution,” IEEE Comput. Graph. Appl. 22(2), 56–65 (2002).
[Crossref]

1948 (1)

D. Gabor, “A new microscopic principle,” Nature 161(4098), 777–778 (1948).
[Crossref] [PubMed]

Abadi, M.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, and M. Isard, “TensorFlow: A System for Large-Scale Machine Learning,” in Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016), pp. 265–283.

Acosta, A.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” in CVPR,  2(3), p. 4 (2017).

Aitken, A. P.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” in CVPR,  2(3), p. 4 (2017).

Albers, J.

S. Schumacher, J. Nestler, T. Otto, M. Wegener, E. Ehrentreich-Förster, D. Michel, K. Wunderlich, S. Palzer, K. Sohn, A. Weber, M. Burgard, A. Grzesiak, A. Teichert, A. Brandenburg, B. Koger, J. Albers, E. Nebling, and F. F. Bier, “Highly-integrated lab-on-chip system for point-of-care multiparameter analysis,” Lab Chip 12(3), 464–473 (2012).
[Crossref] [PubMed]

Allier, C.

C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J. M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry A 91(5), 433–442 (2017).
[Crossref] [PubMed]

C. Fournier, F. Jolivet, L. Denis, N. Verrier, E. Thiebaut, C. Allier, and T. Fournel, “Pixel super-resolution in digital holography by regularized reconstruction,” Appl. Opt. 56(1), 69–77 (2017).
[Crossref]

Aristov, A.

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref] [PubMed]

Barham, P.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, and M. Isard, “TensorFlow: A System for Large-Scale Machine Learning,” in Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016), pp. 265–283.

Bentolila, L.

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Gunaydin, L. Bentolila, and A. Ozcan, “Deep learning achieves super-resolution in fluorescence microscopy,” bioRxiv309641 (2018).

Bhatti, A.

K. Nelson, A. Bhatti, and S. Nahavandi, “Performance Evaluation of Multi-Frame Super-Resolution Algorithms,” in 2012 International Conference on Digital Image Computing Techniques and Applications (DICTA) (IEEE, 2012), pp. 1–8.

Bier, F. F.

S. Schumacher, J. Nestler, T. Otto, M. Wegener, E. Ehrentreich-Förster, D. Michel, K. Wunderlich, S. Palzer, K. Sohn, A. Weber, M. Burgard, A. Grzesiak, A. Teichert, A. Brandenburg, B. Koger, J. Albers, E. Nebling, and F. F. Bier, “Highly-integrated lab-on-chip system for point-of-care multiparameter analysis,” Lab Chip 12(3), 464–473 (2012).
[Crossref] [PubMed]

Bishara, W.

O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010).
[Crossref] [PubMed]

W. Bishara, T.-W. Su, A. F. Coskun, and A. Ozcan, “Lensfree on-chip microscopy over a wide field-of-view using pixel super-resolution,” Opt. Express 18(11), 11181–11191 (2010).
[Crossref] [PubMed]

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Fischer, P.

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Ghemawat, S.

M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, and M. Isard, “TensorFlow: A System for Large-Scale Machine Learning,” in Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016), pp. 265–283.

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C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J. M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry A 91(5), 433–442 (2017).
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Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydın, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5(6), 2354–2364 (2018).
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W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
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C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
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J. Song, C. Leon Swisher, H. Im, S. Jeong, D. Pathania, Y. Iwamoto, M. Pivovarov, R. Weissleder, and H. Lee, “Sparsity-based pixel super resolution for lens-free digital in-line holography,” Sci. Rep. 6, 24681 (2016).
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M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, and M. Isard, “TensorFlow: A System for Large-Scale Machine Learning,” in Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI, 2016), pp. 265–283.

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Jericho, S. K.

Jin, Y.

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A. Greenbaum, W. Luo, B. Khademhosseinieh, T.-W. Su, A. F. Coskun, and A. Ozcan, “Increased space-bandwidth product in pixel super-resolved lensfree on-chip microscopy,” Sci. Rep. 3(1), 1717 (2013).
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L. Lagae, D. Vercruysse, A. Dusa, C. Liu, K. de Wijs, R. Stahl, G. Vanmeerbeeck, B. Majeed, Y. Li, and P. Peumans, “High throughput cell sorter based on lensfree imaging of cells,” in Proceedings of IEEE International Electron Devices Meeting (IEEE, 2015), pp. 333–336.
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J. Song, C. Leon Swisher, H. Im, S. Jeong, D. Pathania, Y. Iwamoto, M. Pivovarov, R. Weissleder, and H. Lee, “Sparsity-based pixel super resolution for lens-free digital in-line holography,” Sci. Rep. 6, 24681 (2016).
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J. Kim, J.K. Lee, and K.M. Lee, “Accurate image super-resolution using very deep convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2016), pp. 1646–1654.
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B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, “Enhanced deep residual networks for single image super-resolution,” in The IEEE conference on computer vision and pattern recognition (CVPR) workshops, vol.  1, p. 4 (2017).

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J. Kim, J.K. Lee, and K.M. Lee, “Accurate image super-resolution using very deep convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2016), pp. 1646–1654.
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W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
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J. Zhang, Q. Chen, J. Li, J. Sun, and C. Zuo, “Lensfree dynamic super-resolved phase imaging based on active micro-scanning,” Opt. Lett. 43(15), 3714–3717 (2018).
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J. Zhang, J. Sun, Q. Chen, J. Li, and C. Zuo, “Adaptive pixel-super-resolved lensfree in-line digital holography for wide-field on-chip microscopy,” Sci. Rep. 7(1), 11777 (2017).
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G. Stybayeva, O. Mudanyali, S. Seo, J. Silangcruz, M. Macal, E. Ramanculov, S. Dandekar, A. Erlinger, A. Ozcan, and A. Revzin, “Lensfree holographic imaging of antibody microarrays for high-throughput detection of leukocyte numbers and function,” Anal. Chem. 82(9), 3736–3744 (2010).
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Teng, D.

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).
[Crossref]

Theis, L.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” in CVPR,  2(3), p. 4 (2017).

Thiebaut, E.

Thurman, S. T.

Tian, L.

Totz, J.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” in CVPR,  2(3), p. 4 (2017).

Tseng, D.

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydın, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5(6), 2354–2364 (2018).
[Crossref]

O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010).
[Crossref] [PubMed]

Tseng, D. K.

S. Seo, T.-W. Su, D. K. Tseng, A. Erlinger, and A. Ozcan, “Lensfree holographic imaging for on-chip cytometry and diagnostics,” Lab Chip 9(6), 777–787 (2009).
[Crossref] [PubMed]

Usson, Y.

C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J. M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry A 91(5), 433–442 (2017).
[Crossref] [PubMed]

Vanmeerbeeck, G.

R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, and et al., “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).

L. Lagae, D. Vercruysse, A. Dusa, C. Liu, K. de Wijs, R. Stahl, G. Vanmeerbeeck, B. Majeed, Y. Li, and P. Peumans, “High throughput cell sorter based on lensfree imaging of cells,” in Proceedings of IEEE International Electron Devices Meeting (IEEE, 2015), pp. 333–336.
[Crossref]

Vercruysse, D.

L. Lagae, D. Vercruysse, A. Dusa, C. Liu, K. de Wijs, R. Stahl, G. Vanmeerbeeck, B. Majeed, Y. Li, and P. Peumans, “High throughput cell sorter based on lensfree imaging of cells,” in Proceedings of IEEE International Electron Devices Meeting (IEEE, 2015), pp. 333–336.
[Crossref]

Verrier, N.

Vincent, R.

C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J. M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry A 91(5), 433–442 (2017).
[Crossref] [PubMed]

Wang, H.

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydın, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5(6), 2354–2364 (2018).
[Crossref]

Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4(11), 1437–1443 (2017).
[Crossref]

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Gunaydin, L. Bentolila, and A. Ozcan, “Deep learning achieves super-resolution in fluorescence microscopy,” bioRxiv309641 (2018).

Wang, L.

Y. Huang, W. Wang, and L. Wang, “Bidirectional recurrent convolutional networks for multi-frame super-resolution,” Adv. Neural Inf. Process. Syst. 28235–243 (2015).

Wang, W.

Y. Huang, W. Wang, and L. Wang, “Bidirectional recurrent convolutional networks for multi-frame super-resolution,” Adv. Neural Inf. Process. Syst. 28235–243 (2015).

Wang, Z.

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” in CVPR,  2(3), p. 4 (2017).

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

Weber, A.

S. Schumacher, J. Nestler, T. Otto, M. Wegener, E. Ehrentreich-Förster, D. Michel, K. Wunderlich, S. Palzer, K. Sohn, A. Weber, M. Burgard, A. Grzesiak, A. Teichert, A. Brandenburg, B. Koger, J. Albers, E. Nebling, and F. F. Bier, “Highly-integrated lab-on-chip system for point-of-care multiparameter analysis,” Lab Chip 12(3), 464–473 (2012).
[Crossref] [PubMed]

Wegener, M.

S. Schumacher, J. Nestler, T. Otto, M. Wegener, E. Ehrentreich-Förster, D. Michel, K. Wunderlich, S. Palzer, K. Sohn, A. Weber, M. Burgard, A. Grzesiak, A. Teichert, A. Brandenburg, B. Koger, J. Albers, E. Nebling, and F. F. Bier, “Highly-integrated lab-on-chip system for point-of-care multiparameter analysis,” Lab Chip 12(3), 464–473 (2012).
[Crossref] [PubMed]

Wei, Z.

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydın, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5(6), 2354–2364 (2018).
[Crossref]

H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Gunaydin, L. Bentolila, and A. Ozcan, “Deep learning achieves super-resolution in fluorescence microscopy,” bioRxiv309641 (2018).

Weiss, L. E.

Weissleder, R.

J. Song, C. Leon Swisher, H. Im, S. Jeong, D. Pathania, Y. Iwamoto, M. Pivovarov, R. Weissleder, and H. Lee, “Sparsity-based pixel super resolution for lens-free digital in-line holography,” Sci. Rep. 6, 24681 (2016).
[Crossref] [PubMed]

Wunderlich, K.

S. Schumacher, J. Nestler, T. Otto, M. Wegener, E. Ehrentreich-Förster, D. Michel, K. Wunderlich, S. Palzer, K. Sohn, A. Weber, M. Burgard, A. Grzesiak, A. Teichert, A. Brandenburg, B. Koger, J. Albers, E. Nebling, and F. F. Bier, “Highly-integrated lab-on-chip system for point-of-care multiparameter analysis,” Lab Chip 12(3), 464–473 (2012).
[Crossref] [PubMed]

Xu, W.

Xue, Y.

Yashiro, M.

R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, and et al., “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).

Zhang, J.

J. Zhang, Q. Chen, J. Li, J. Sun, and C. Zuo, “Lensfree dynamic super-resolved phase imaging based on active micro-scanning,” Opt. Lett. 43(15), 3714–3717 (2018).
[Crossref] [PubMed]

J. Zhang, J. Sun, Q. Chen, J. Li, and C. Zuo, “Adaptive pixel-super-resolved lensfree in-line digital holography for wide-field on-chip microscopy,” Sci. Rep. 7(1), 11777 (2017).
[Crossref] [PubMed]

Zhang, K.

K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Trans. Image Process. 26(7), 3142–3155 (2017).
[Crossref] [PubMed]

Zhang, L.

K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Trans. Image Process. 26(7), 3142–3155 (2017).
[Crossref] [PubMed]

Zhang, Y.

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).
[Crossref]

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydın, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5(6), 2354–2364 (2018).
[Crossref]

Y. Rivenson, Z. Göröcs, H. Günaydin, Y. Zhang, H. Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4(11), 1437–1443 (2017).
[Crossref]

W. Luo, Y. Zhang, A. Feizi, Z. Göröcs, and A. Ozcan, “Pixel super-resolution using wavelength scanning,” Light Sci. Appl. 5(4), e16060 (2016).
[Crossref] [PubMed]

W. Luo, Y. Zhang, Z. Göröcs, A. Feizi, and A. Ozcan, “Propagation phasor approach for holographic image reconstruction,” Sci. Rep. 6(1), 22738 (2016).
[Crossref] [PubMed]

W. Luo, A. Greenbaum, Y. Zhang, and A. Ozcan, “Synthetic aperture-based on-chip microscopy,” Light Sci. Appl. 4(3), e261 (2015).
[Crossref]

Zhu, H.

Zimmer, C.

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref] [PubMed]

Zuo, C.

J. Zhang, Q. Chen, J. Li, J. Sun, and C. Zuo, “Lensfree dynamic super-resolved phase imaging based on active micro-scanning,” Opt. Lett. 43(15), 3714–3717 (2018).
[Crossref] [PubMed]

J. Zhang, J. Sun, Q. Chen, J. Li, and C. Zuo, “Adaptive pixel-super-resolved lensfree in-line digital holography for wide-field on-chip microscopy,” Sci. Rep. 7(1), 11777 (2017).
[Crossref] [PubMed]

Zuo, W.

K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Trans. Image Process. 26(7), 3142–3155 (2017).
[Crossref] [PubMed]

ACS Photonics (1)

Y. Rivenson, H. Ceylan Koydemir, H. Wang, Z. Wei, Z. Ren, H. Günaydın, Y. Zhang, Z. Gorocs, K. Liang, D. Tseng, and A. Ozcan, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5(6), 2354–2364 (2018).
[Crossref]

Adv. Neural Inf. Process. Syst. (1)

Y. Huang, W. Wang, and L. Wang, “Bidirectional recurrent convolutional networks for multi-frame super-resolution,” Adv. Neural Inf. Process. Syst. 28235–243 (2015).

Anal. Chem. (1)

G. Stybayeva, O. Mudanyali, S. Seo, J. Silangcruz, M. Macal, E. Ramanculov, S. Dandekar, A. Erlinger, A. Ozcan, and A. Revzin, “Lensfree holographic imaging of antibody microarrays for high-throughput detection of leukocyte numbers and function,” Anal. Chem. 82(9), 3736–3744 (2010).
[Crossref] [PubMed]

Appl. Opt. (2)

Cytometry A (1)

C. Allier, S. Morel, R. Vincent, L. Ghenim, F. Navarro, M. Menneteau, T. Bordy, L. Hervé, O. Cioni, X. Gidrol, Y. Usson, and J. M. Dinten, “Imaging of dense cell cultures by multiwavelength lens-free video microscopy,” Cytometry A 91(5), 433–442 (2017).
[Crossref] [PubMed]

IEEE Comput. Graph. Appl. (1)

W. T. Freeman, T. R. Jones, and E. C. Pasztor, “Example-based super-resolution,” IEEE Comput. Graph. Appl. 22(2), 56–65 (2002).
[Crossref]

IEEE Trans. Image Process. (3)

K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,” IEEE Trans. Image Process. 26(7), 3142–3155 (2017).
[Crossref] [PubMed]

S. Farsiu, M. D. Robinson, M. Elad, and P. Milanfar, “Fast and robust multiframe super resolution,” IEEE Trans. Image Process. 13(10), 1327–1344 (2004).
[Crossref] [PubMed]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 600–612 (2004).
[Crossref] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

C. Dong, C. C. Loy, K. He, and X. Tang, “Image super-resolution using deep convolutional networks,” IEEE Trans. Pattern Anal. Mach. Intell. 38(2), 295–307 (2016).
[Crossref] [PubMed]

in CVPR (1)

C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and et al., “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” in CVPR,  2(3), p. 4 (2017).

in The IEEE conference on computer vision and pattern recognition (CVPR) workshops (1)

B. Lim, S. Son, H. Kim, S. Nah, and K. M. Lee, “Enhanced deep residual networks for single image super-resolution,” in The IEEE conference on computer vision and pattern recognition (CVPR) workshops, vol.  1, p. 4 (2017).

Int. J. Imaging Syst. Technol. (1)

Y. Lu, M. Inamura, and M. del Carmen Valdes, “Super-resolution of the undersampled and subpixel shifted image sequence by a neural network,” Int. J. Imaging Syst. Technol. 14(1), 8–15 (2004).
[Crossref]

Lab Chip (4)

A. Ozcan and U. Demirci, “Ultra wide-field lens-free monitoring of cells on-chip,” Lab Chip 8(1), 98–106 (2008).
[Crossref] [PubMed]

S. Schumacher, J. Nestler, T. Otto, M. Wegener, E. Ehrentreich-Förster, D. Michel, K. Wunderlich, S. Palzer, K. Sohn, A. Weber, M. Burgard, A. Grzesiak, A. Teichert, A. Brandenburg, B. Koger, J. Albers, E. Nebling, and F. F. Bier, “Highly-integrated lab-on-chip system for point-of-care multiparameter analysis,” Lab Chip 12(3), 464–473 (2012).
[Crossref] [PubMed]

O. Mudanyali, D. Tseng, C. Oh, S. O. Isikman, I. Sencan, W. Bishara, C. Oztoprak, S. Seo, B. Khademhosseini, and A. Ozcan, “Compact, light-weight and cost-effective microscope based on lensless incoherent holography for telemedicine applications,” Lab Chip 10(11), 1417–1428 (2010).
[Crossref] [PubMed]

S. Seo, T.-W. Su, D. K. Tseng, A. Erlinger, and A. Ozcan, “Lensfree holographic imaging for on-chip cytometry and diagnostics,” Lab Chip 9(6), 777–787 (2009).
[Crossref] [PubMed]

Light Sci. Appl. (3)

W. Luo, Y. Zhang, A. Feizi, Z. Göröcs, and A. Ozcan, “Pixel super-resolution using wavelength scanning,” Light Sci. Appl. 5(4), e16060 (2016).
[Crossref] [PubMed]

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).
[Crossref]

W. Luo, A. Greenbaum, Y. Zhang, and A. Ozcan, “Synthetic aperture-based on-chip microscopy,” Light Sci. Appl. 4(3), e261 (2015).
[Crossref]

Nat. Biotechnol. (1)

W. Ouyang, A. Aristov, M. Lelek, X. Hao, and C. Zimmer, “Deep learning massively accelerates super-resolution localization microscopy,” Nat. Biotechnol. 36(5), 460–468 (2018).
[Crossref] [PubMed]

Nature (1)

D. Gabor, “A new microscopic principle,” Nature 161(4098), 777–778 (1948).
[Crossref] [PubMed]

Opt. Express (5)

Opt. Lett. (2)

Optica (2)

Proc. SPIE (1)

R. Stahl, G. Vanmeerbeeck, G. Lafruit, R. Huys, V. Reumers, A. Lambrechts, C.-K. Liao, C.-C. Hsiao, M. Yashiro, M. Takemoto, and et al., “Lens-free digital in-line holographic imaging for wide field-of-view, high-resolution and real-time monitoring of complex microscopic objects,” Proc. SPIE 8947, 89471F (2014).

Rep. Prog. Phys. (1)

E. McLeod and A. Ozcan, “Unconventional methods of imaging: computational microscopy and compact implementations,” Rep. Prog. Phys. 79(7), 076001 (2016).
[Crossref] [PubMed]

Sci. Rep. (4)

J. Song, C. Leon Swisher, H. Im, S. Jeong, D. Pathania, Y. Iwamoto, M. Pivovarov, R. Weissleder, and H. Lee, “Sparsity-based pixel super resolution for lens-free digital in-line holography,” Sci. Rep. 6, 24681 (2016).
[Crossref] [PubMed]

J. Zhang, J. Sun, Q. Chen, J. Li, and C. Zuo, “Adaptive pixel-super-resolved lensfree in-line digital holography for wide-field on-chip microscopy,” Sci. Rep. 7(1), 11777 (2017).
[Crossref] [PubMed]

A. Greenbaum, W. Luo, B. Khademhosseinieh, T.-W. Su, A. F. Coskun, and A. Ozcan, “Increased space-bandwidth product in pixel super-resolved lensfree on-chip microscopy,” Sci. Rep. 3(1), 1717 (2013).
[Crossref]

W. Luo, Y. Zhang, Z. Göröcs, A. Feizi, and A. Ozcan, “Propagation phasor approach for holographic image reconstruction,” Sci. Rep. 6(1), 22738 (2016).
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L. Lagae, D. Vercruysse, A. Dusa, C. Liu, K. de Wijs, R. Stahl, G. Vanmeerbeeck, B. Majeed, Y. Li, and P. Peumans, “High throughput cell sorter based on lensfree imaging of cells,” in Proceedings of IEEE International Electron Devices Meeting (IEEE, 2015), pp. 333–336.
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H. Wang, Y. Rivenson, Y. Jin, Z. Wei, R. Gao, H. Gunaydin, L. Bentolila, and A. Ozcan, “Deep learning achieves super-resolution in fluorescence microscopy,” bioRxiv309641 (2018).

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Figures (10)

Fig. 1
Fig. 1 Overview of the deep learning-based pixel super-resolution approach. The CNN is trained to take a stack of LR holograms with subpixel shift as input and produce the corresponding HR hologram as output. The training data set is generated based on the single frame hologram captured by the LFHM which serves as the “HR ground truth” itself. The corresponding LR image sequence is synthesized by digitally shifting and down-sampling this hologram.
Fig. 2
Fig. 2 Image acquisition of LR holograms with sub-pixel shift. (a) The schematic diagram of the PSR LFHM experiment setup. Shifting the light source in lateral directions leads to sub-pixel shift of the hologram at the sensor plane. (b) Geometric relationship between the light source shifting and the hologram shifting. (c) Itinerary of the hologram sub-pixel shift where LR grid corresponds to the physical pixel size of the imager and HR grid represents the effective pixel size of the HR hologram.
Fig. 3
Fig. 3 The schematic diagram of the LR sequence synthesis process. A series of LR holograms is generated by repeatedly applying geometric warping, blurring and decimation with different lateral shifts to the single frame hologram captured by the LFHM.
Fig. 4
Fig. 4 Accuracy of the model during the training. (a) training/test accuracy with respect to the number of training iterations of the neural network using 25 holograms as input. (b) training/test accuracy with respect to the number of training iterations of the neural network using 9 holograms as input.
Fig. 5
Fig. 5 Deep learning-based PSR of the USAF resolution test chart. (a) The interpolated LR hologram of the USAF resolution test chart. (b) The enlarged image within the yellow dashed box. (c) Reconstructed amplitude image from the interpolated LR hologram. The first 4 elements of group 9 of the chart is shown in the green dashed box. (d) HR hologram predicted by the trained CNN within the yellow dashed box. (e) Reconstructed amplitude image from the HR hologram. (f), (g) Cross-section profiles of group 8, element 5 and element 6 of the LR reconstructed image. (h), (i) Cross-section profiles of group 9, element 3 and element 4 of the HR reconstructed image. (j) Ground truth of the resolution chart provided by a 40X 0.5NA objective. (k) MTFs obtained by the baseline method (LR reconstruction), the deep learning-based method (HR reconstruction) and the 40X 0.5NA objective.
Fig. 6
Fig. 6 Comparison of resolution improvement by different approaches. (a) Comparison between the reconstructed amplitude image by different approaches. The results from the bicubic interpolation, the deep learning-based PSR method using 25 holograms and 9 holograms, the iterative PSR method using 25 holograms and 9 holograms are denoted by Bicubic, CNNPSR25, CNNPSR9, IPSR25 and IPSR9 respectively. (b) – (d) Cross section profiles of group 9, element 3 between CNNPSR25 and IPSR25, CNNPSR25 and CNNPSR9, IPSR25 and IPSR9 respectively.
Fig. 7
Fig. 7 FWHM measurement of polystyrene beads. (a) The reconstructed amplitude image of the polystyrene beads based on the network prediction. (b), (c) The enlarged image of two beads from CNNPSR25 and Bicubic. (d), (e) Cross section profiles along the arrows in the blue and red dashed box. (f) The averaged amplitude image of 108 beads from CNNPSR25. (g) Comparison between the FWHMs of various approaches.
Fig. 8
Fig. 8 Super-resolution lens-free imaging of lily anther. (a) The reconstructed phase image of the lily anther using deep learning-based PSR method. The yellow box corresponds to the FOV of a 10X microscope objective. Scale bar: 200 μm. (b), (c) The enlarged image within the yellow dashed box from the HR lens-free reconstruction and 10X objective respectively. Scale bar: 100 μm. (d) - (f) The zoom-in images of the red dashed box area of the baseline LR lens-free image, the HR lens-free image and the ground truth (GT) image from the 40X objective respectively. Scale bar: 15 μm. (g) Cross-section profiles along the arrows in (d) - (f).
Fig. 9
Fig. 9 Influence of the training data set generated with different types of samples. (a) Holograms of different types of samples and their corresponding sample-to-sensor distances (denoted by d ss ). Scale bar: 50 μm. (b) Cross-section profiles of group 9, element 3 of the USAF resolution based on the HR hologram obtained by networks trained on different types of samples. 25 holograms were used. (c) Same as (b) but only 9 holograms were used. (d) FWHM of the polystyrene beads based on the HR hologram obtained by networks trained on different types of samples.
Fig. 10
Fig. 10 The schematic diagram of the CNN architecture.

Tables (2)

Tables Icon

Table 1 SSIMs of the images reconstructed by the baseline approach and the deep learning approach with respect to the result from the iterative PSR approach

Tables Icon

Table 2 Run-time profiling of the deep learning-based PSR methods and the iterative PSR methods

Equations (4)

Equations on this page are rendered with MathJax. Learn more.

d x 1 d x 2 = d y 1 d y 2 = z 1 z 2
SSIM(x,y)= (2 μ x μ y + c 1 )(2 σ xy + c 2 ) ( μ x 2 + μ y 2 + c 1 )( σ x 2 + σ y 2 + c 2 )
Y k =DH F k X+ V k k=1,2,...,N
X=arg min X [ k=1 N DH F k X Y k 1 +λ X BTV ]

Metrics