Abstract

We combine a generative adversarial network (GAN) with light microscopy to achieve deep learning super-resolution under a large field of view (FOV). By appropriately adopting prior microscopy data in an adversarial training, the neural network can recover a high-resolution, accurate image of new specimen from its single low-resolution measurement. Its capacity has been broadly demonstrated via imaging various types of samples, such as USAF resolution target, human pathological slides, fluorescence-labelled fibroblast cells, and deep tissues in transgenic mouse brain, by both wide-field and light-sheet microscopes. The gigapixel, multi-color reconstruction of these samples verifies a successful GAN-based single image super-resolution procedure. We also propose an image degrading model to generate low resolution images for training, making our approach free from the complex image registration during training data set preparation. After a well-trained network has been created, this deep learning-based imaging approach is capable of recovering a large FOV (~95 mm2) enhanced resolution of ~1.7 μm at high speed (within 1 second), while not necessarily introducing any changes to the setup of existing microscopes.

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

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References

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2018 (2)

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]

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

2017 (2)

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

Y. Rivenson, H. C. Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydin, Y. Zhang, Z. Gorocs, K. Liang, and D. Tseng, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2017).

2016 (4)

G. Litjens, C. I. Sánchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen-van de Kaa, P. Bult, B. van Ginneken, and J. van der Laak, “Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis,” Sci. Rep. 6(1), 26286 (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]

Z. Guan, J. Lee, H. Jiang, S. Dong, N. Jen, T. Hsiai, C. M. Ho, and P. Fei, “Compact plane illumination plugin device to enable light sheet fluorescence imaging of multi-cellular organisms on an inverted wide-field microscope,” Biomed. Opt. Express 7(1), 194–208 (2016).
[Crossref] [PubMed]

W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional,” Neural Netw. 2016, 1874–1883 (2016).

2015 (1)

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

2013 (2)

A. Greenbaum, W. Luo, B. Khademhosseinieh, T. 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]

G. Zheng, R. Horstmeyer, and C. Yang, “Wide-field, high-resolution Fourier ptychographic microscopy,” Nat. Photonics 7(9), 739–745 (2013).
[Crossref] [PubMed]

2011 (2)

G. Zheng, S. A. Lee, Y. Antebi, M. B. Elowitz, and C. Yang, “The ePetri dish, an on-chip cell imaging platform based on subpixel perspective sweeping microscopy (SPSM),” Proc. Natl. Acad. Sci. U.S.A. 108(41), 16889–16894 (2011).
[Crossref] [PubMed]

M. Kim, Y. Choi, C. Fang-Yen, Y. Sung, R. R. Dasari, M. S. Feld, and W. Choi, “High-speed synthetic aperture microscopy for live cell imaging,” Opt. Lett. 36(2), 148–150 (2011).
[Crossref] [PubMed]

2010 (2)

2009 (2)

2008 (4)

P. J. Keller, A. D. Schmidt, J. Wittbrodt, and E. H. K. Stelzer, “Reconstruction of Zebrafish Early Embryonic Development by Scanned Light Sheet Microscopy,” Science 322(5904), 1065–1069 (2008).
[Crossref] [PubMed]

K. Becker, N. Jährling, E. R. Kramer, F. Schnorrer, and H. U. Dodt, “Ultramicroscopy: 3D reconstruction of large microscopical specimens,” J. Biophotonics 1(1), 36–42 (2008).
[Crossref] [PubMed]

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

2007 (2)

M. Brown and D. G. Lowe, “Automatic panoramic image stitching using invariant features,” Int. J. Comput. Vis. 74(1), 59–73 (2007).
[Crossref]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

2006 (1)

P. Vandewalle, S. Sü, and M. Vetterli, “A frequency domain approach to registration of aliased images with application to super-resolution,” EURASIP J. Adv. Signal Process. 2006, 1–14 (2006).

2005 (1)

M. G. Gustafsson, “Nonlinear structured-illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution,” Proc. Natl. Acad. Sci. U.S.A. 102(37), 13081–13086 (2005).
[Crossref] [PubMed]

2004 (2)

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]

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

2001 (2)

M. Elad and Y. Hel-Or, “A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur,” IEEE Trans. Image Process. 10(8), 1187–1193 (2001).
[Crossref] [PubMed]

W. Xu, M. H. Jericho, I. A. Meinertzhagen, and H. J. Kreuzer, “Digital in-line holography for biological applications,” Proc. Natl. Acad. Sci. U.S.A. 98(20), 11301–11305 (2001).
[Crossref] [PubMed]

2000 (1)

M. G. L. Gustafsson, “Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy,” J. Microsc. 198(Pt 2), 82–87 (2000).
[Crossref] [PubMed]

1996 (1)

Acosta, A.

C. Ledig, Z. Wang, W. Shi, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, and A. Tejani, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” https://arxiv.org/abs/1609.04802 (2016).

Aitken, A.

C. Ledig, Z. Wang, W. Shi, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, and A. Tejani, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” https://arxiv.org/abs/1609.04802 (2016).

Aitken, A. P.

W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional,” Neural Netw. 2016, 1874–1883 (2016).

Alexandrov, S. A.

Antebi, Y.

G. Zheng, S. A. Lee, Y. Antebi, M. B. Elowitz, and C. Yang, “The ePetri dish, an on-chip cell imaging platform based on subpixel perspective sweeping microscopy (SPSM),” Proc. Natl. Acad. Sci. U.S.A. 108(41), 16889–16894 (2011).
[Crossref] [PubMed]

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]

Becker, K.

K. Becker, N. Jährling, E. R. Kramer, F. Schnorrer, and H. U. Dodt, “Ultramicroscopy: 3D reconstruction of large microscopical specimens,” J. Biophotonics 1(1), 36–42 (2008).
[Crossref] [PubMed]

Bengio, Y.

I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in International Conference on Neural Information Processing Systems(2014), pp. 2672–2680.

Berdondini, L.

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

G. Hilgen, S. Pirmoradian, A. Maccione, L. Berdondini, M. H. Hennig, and E. Sernagor, “High Resolution Large-Scale Recordings of Light Responses from Ganglion Cells in the Developing Mouse Retina,” in FASEB Conference on Retinal Neurobiology and Visual Processing(2014).

Bishop, R.

W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional,” Neural Netw. 2016, 1874–1883 (2016).

Bornat, Y.

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

Brown, M.

M. Brown and D. G. Lowe, “Automatic panoramic image stitching using invariant features,” Int. J. Comput. Vis. 74(1), 59–73 (2007).
[Crossref]

Bult, P.

G. Litjens, C. I. Sánchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen-van de Kaa, P. Bult, B. van Ginneken, and J. van der Laak, “Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis,” Sci. Rep. 6(1), 26286 (2016).
[Crossref] [PubMed]

Caballero, J.

W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional,” Neural Netw. 2016, 1874–1883 (2016).

C. Ledig, Z. Wang, W. Shi, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, and A. Tejani, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” https://arxiv.org/abs/1609.04802 (2016).

Choi, W.

Choi, Y.

Coskun, A. F.

A. Greenbaum, W. Luo, B. Khademhosseinieh, T. 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]

Courville, A.

I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in International Conference on Neural Information Processing Systems(2014), pp. 2672–2680.

Cunningham, A.

C. Ledig, Z. Wang, W. Shi, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, and A. Tejani, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” https://arxiv.org/abs/1609.04802 (2016).

Dabov, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Color Image Denoising via Sparse 3D Collaborative Filtering with Grouping Constraint in Luminance-Chrominance Space,” in IEEE International Conference on Image Processing(2007), pp. 313–316.
[Crossref]

Dasari, R. R.

Denis, L.

Dodt, H. U.

K. Becker, N. Jährling, E. R. Kramer, F. Schnorrer, and H. U. Dodt, “Ultramicroscopy: 3D reconstruction of large microscopical specimens,” J. Biophotonics 1(1), 36–42 (2008).
[Crossref] [PubMed]

Dong, S.

Dorsch, R. G.

Egiazarian, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Color Image Denoising via Sparse 3D Collaborative Filtering with Grouping Constraint in Luminance-Chrominance Space,” in IEEE International Conference on Image Processing(2007), pp. 313–316.
[Crossref]

Elad, M.

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]

M. Elad and Y. Hel-Or, “A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur,” IEEE Trans. Image Process. 10(8), 1187–1193 (2001).
[Crossref] [PubMed]

Elowitz, M. B.

G. Zheng, S. A. Lee, Y. Antebi, M. B. Elowitz, and C. Yang, “The ePetri dish, an on-chip cell imaging platform based on subpixel perspective sweeping microscopy (SPSM),” Proc. Natl. Acad. Sci. U.S.A. 108(41), 16889–16894 (2011).
[Crossref] [PubMed]

Ertosun, M. G.

M. G. Ertosun and D. L. Rubin, “Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks,” AMIA Symposium2015, 1899 (2015).

Fang, C.

H. Zhang, X. Xie, C. Fang, Y. Yang, D. Jin, and P. Fei, “High-throughput, high-resolution Generated Adversarial Network Microscopy,” https://arxiv.org/abs/1801.07330 (2018).

Fang-Yen, C.

Farine, P. A.

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

Farsiu, S.

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]

Fei, P.

Feizi, A.

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]

Feld, M. S.

Ferreira, C.

Foi, A.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Color Image Denoising via Sparse 3D Collaborative Filtering with Grouping Constraint in Luminance-Chrominance Space,” in IEEE International Conference on Image Processing(2007), pp. 313–316.
[Crossref]

Fournier, C.

Goodfellow, I. J.

I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in International Conference on Neural Information Processing Systems(2014), pp. 2672–2680.

Gorocs, Z.

Y. Rivenson, H. C. Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydin, Y. Zhang, Z. Gorocs, K. Liang, and D. Tseng, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2017).

Göröcs, Z.

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

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]

Greenbaum, A.

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

A. Greenbaum, W. Luo, B. Khademhosseinieh, T. 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]

Guan, Z.

Gunaydin, H.

Y. Rivenson, H. C. Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydin, Y. Zhang, Z. Gorocs, K. Liang, and D. Tseng, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2017).

Günaydin, H.

Gustafsson, M. G.

M. G. Gustafsson, “Nonlinear structured-illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution,” Proc. Natl. Acad. Sci. U.S.A. 102(37), 13081–13086 (2005).
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Gustafsson, M. G. L.

M. G. L. Gustafsson, “Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy,” J. Microsc. 198(Pt 2), 82–87 (2000).
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Gutzler, T.

Hao, X.

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]

Hel-Or, Y.

M. Elad and Y. Hel-Or, “A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur,” IEEE Trans. Image Process. 10(8), 1187–1193 (2001).
[Crossref] [PubMed]

Hennig, M. H.

G. Hilgen, S. Pirmoradian, A. Maccione, L. Berdondini, M. H. Hennig, and E. Sernagor, “High Resolution Large-Scale Recordings of Light Responses from Ganglion Cells in the Developing Mouse Retina,” in FASEB Conference on Retinal Neurobiology and Visual Processing(2014).

Hermsen, M.

G. Litjens, C. I. Sánchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen-van de Kaa, P. Bult, B. van Ginneken, and J. van der Laak, “Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis,” Sci. Rep. 6(1), 26286 (2016).
[Crossref] [PubMed]

Hilgen, G.

G. Hilgen, S. Pirmoradian, A. Maccione, L. Berdondini, M. H. Hennig, and E. Sernagor, “High Resolution Large-Scale Recordings of Light Responses from Ganglion Cells in the Developing Mouse Retina,” in FASEB Conference on Retinal Neurobiology and Visual Processing(2014).

Hillman, T. R.

Ho, C. M.

Horstmeyer, R.

G. Zheng, R. Horstmeyer, and C. Yang, “Wide-field, high-resolution Fourier ptychographic microscopy,” Nat. Photonics 7(9), 739–745 (2013).
[Crossref] [PubMed]

Hsiai, T.

Hulsbergen-van de Kaa, C.

G. Litjens, C. I. Sánchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen-van de Kaa, P. Bult, B. van Ginneken, and J. van der Laak, “Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis,” Sci. Rep. 6(1), 26286 (2016).
[Crossref] [PubMed]

Huszar, F.

W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional,” Neural Netw. 2016, 1874–1883 (2016).

C. Ledig, Z. Wang, W. Shi, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, and A. Tejani, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” https://arxiv.org/abs/1609.04802 (2016).

Imfeld, K.

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

Jährling, N.

K. Becker, N. Jährling, E. R. Kramer, F. Schnorrer, and H. U. Dodt, “Ultramicroscopy: 3D reconstruction of large microscopical specimens,” J. Biophotonics 1(1), 36–42 (2008).
[Crossref] [PubMed]

Jen, N.

Jericho, M. H.

W. Xu, M. H. Jericho, I. A. Meinertzhagen, and H. J. Kreuzer, “Digital in-line holography for biological applications,” Proc. Natl. Acad. Sci. U.S.A. 98(20), 11301–11305 (2001).
[Crossref] [PubMed]

Jiang, H.

Jin, D.

H. Zhang, X. Xie, C. Fang, Y. Yang, D. Jin, and P. Fei, “High-throughput, high-resolution Generated Adversarial Network Microscopy,” https://arxiv.org/abs/1801.07330 (2018).

Katkovnik, V.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
[Crossref] [PubMed]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Color Image Denoising via Sparse 3D Collaborative Filtering with Grouping Constraint in Luminance-Chrominance Space,” in IEEE International Conference on Image Processing(2007), pp. 313–316.
[Crossref]

Keller, P. J.

P. J. Keller, A. D. Schmidt, J. Wittbrodt, and E. H. K. Stelzer, “Reconstruction of Zebrafish Early Embryonic Development by Scanned Light Sheet Microscopy,” Science 322(5904), 1065–1069 (2008).
[Crossref] [PubMed]

Khademhosseinieh, B.

A. Greenbaum, W. Luo, B. Khademhosseinieh, T. 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]

Kim, M.

Koudelka-Hep, M.

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

Kovacs, I.

G. Litjens, C. I. Sánchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen-van de Kaa, P. Bult, B. van Ginneken, and J. van der Laak, “Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis,” Sci. Rep. 6(1), 26286 (2016).
[Crossref] [PubMed]

Koydemir, H. C.

Y. Rivenson, H. C. Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydin, Y. Zhang, Z. Gorocs, K. Liang, and D. Tseng, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2017).

Kramer, E. R.

K. Becker, N. Jährling, E. R. Kramer, F. Schnorrer, and H. U. Dodt, “Ultramicroscopy: 3D reconstruction of large microscopical specimens,” J. Biophotonics 1(1), 36–42 (2008).
[Crossref] [PubMed]

Kreuzer, H. J.

W. Xu, M. H. Jericho, I. A. Meinertzhagen, and H. J. Kreuzer, “Digital in-line holography for biological applications,” Proc. Natl. Acad. Sci. U.S.A. 98(20), 11301–11305 (2001).
[Crossref] [PubMed]

Ledig, C.

C. Ledig, Z. Wang, W. Shi, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, and A. Tejani, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” https://arxiv.org/abs/1609.04802 (2016).

Lee, J.

Lee, S. A.

G. Zheng, S. A. Lee, Y. Antebi, M. B. Elowitz, and C. Yang, “The ePetri dish, an on-chip cell imaging platform based on subpixel perspective sweeping microscopy (SPSM),” Proc. Natl. Acad. Sci. U.S.A. 108(41), 16889–16894 (2011).
[Crossref] [PubMed]

G. Zheng, S. A. Lee, S. Yang, and C. Yang, “Sub-pixel resolving optofluidic microscope for on-chip cell imaging,” Lab Chip 10(22), 3125–3129 (2010).
[Crossref] [PubMed]

Lelek, M.

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]

Liang, K.

Y. Rivenson, H. C. Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydin, Y. Zhang, Z. Gorocs, K. Liang, and D. Tseng, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2017).

Litjens, G.

G. Litjens, C. I. Sánchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen-van de Kaa, P. Bult, B. van Ginneken, and J. van der Laak, “Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis,” Sci. Rep. 6(1), 26286 (2016).
[Crossref] [PubMed]

Lohmann, A. W.

Lorenz, D.

Lowe, D. G.

M. Brown and D. G. Lowe, “Automatic panoramic image stitching using invariant features,” Int. J. Comput. Vis. 74(1), 59–73 (2007).
[Crossref]

Luo, W.

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

A. Greenbaum, W. Luo, B. Khademhosseinieh, T. 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]

Maccione, A.

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

G. Hilgen, S. Pirmoradian, A. Maccione, L. Berdondini, M. H. Hennig, and E. Sernagor, “High Resolution Large-Scale Recordings of Light Responses from Ganglion Cells in the Developing Mouse Retina,” in FASEB Conference on Retinal Neurobiology and Visual Processing(2014).

Martinoia, S.

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

Meinertzhagen, I. A.

W. Xu, M. H. Jericho, I. A. Meinertzhagen, and H. J. Kreuzer, “Digital in-line holography for biological applications,” Proc. Natl. Acad. Sci. U.S.A. 98(20), 11301–11305 (2001).
[Crossref] [PubMed]

Mendlovic, D.

Michaeli, T.

Milanfar, P.

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]

Mirza, M.

I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in International Conference on Neural Information Processing Systems(2014), pp. 2672–2680.

Nagtegaal, I.

G. Litjens, C. I. Sánchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen-van de Kaa, P. Bult, B. van Ginneken, and J. van der Laak, “Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis,” Sci. Rep. 6(1), 26286 (2016).
[Crossref] [PubMed]

Nehme, E.

Neukom, S.

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
[Crossref] [PubMed]

Ouyang, W.

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]

Ozair, S.

I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in International Conference on Neural Information Processing Systems(2014), pp. 2672–2680.

Ozcan, A.

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

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

A. Greenbaum, W. Luo, B. Khademhosseinieh, T. 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]

Pirmoradian, S.

G. Hilgen, S. Pirmoradian, A. Maccione, L. Berdondini, M. H. Hennig, and E. Sernagor, “High Resolution Large-Scale Recordings of Light Responses from Ganglion Cells in the Developing Mouse Retina,” in FASEB Conference on Retinal Neurobiology and Visual Processing(2014).

Pouget-Abadie, J.

I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in International Conference on Neural Information Processing Systems(2014), pp. 2672–2680.

Ren, Z.

Y. Rivenson, H. C. Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydin, Y. Zhang, Z. Gorocs, K. Liang, and D. Tseng, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2017).

Rivenson, Y.

Y. Rivenson, H. C. Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydin, Y. Zhang, Z. Gorocs, K. Liang, and D. Tseng, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2017).

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

Robinson, M. D.

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]

Rubin, D. L.

M. G. Ertosun and D. L. Rubin, “Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks,” AMIA Symposium2015, 1899 (2015).

Rueckert, D.

W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional,” Neural Netw. 2016, 1874–1883 (2016).

Sampson, D. D.

Sánchez, C. I.

G. Litjens, C. I. Sánchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen-van de Kaa, P. Bult, B. van Ginneken, and J. van der Laak, “Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis,” Sci. Rep. 6(1), 26286 (2016).
[Crossref] [PubMed]

Schmidt, A. D.

P. J. Keller, A. D. Schmidt, J. Wittbrodt, and E. H. K. Stelzer, “Reconstruction of Zebrafish Early Embryonic Development by Scanned Light Sheet Microscopy,” Science 322(5904), 1065–1069 (2008).
[Crossref] [PubMed]

Schnorrer, F.

K. Becker, N. Jährling, E. R. Kramer, F. Schnorrer, and H. U. Dodt, “Ultramicroscopy: 3D reconstruction of large microscopical specimens,” J. Biophotonics 1(1), 36–42 (2008).
[Crossref] [PubMed]

Sernagor, E.

G. Hilgen, S. Pirmoradian, A. Maccione, L. Berdondini, M. H. Hennig, and E. Sernagor, “High Resolution Large-Scale Recordings of Light Responses from Ganglion Cells in the Developing Mouse Retina,” in FASEB Conference on Retinal Neurobiology and Visual Processing(2014).

Shechtman, Y.

Sheikh, H. R.

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

Shi, W.

W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional,” Neural Netw. 2016, 1874–1883 (2016).

C. Ledig, Z. Wang, W. Shi, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, and A. Tejani, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” https://arxiv.org/abs/1609.04802 (2016).

Simoncelli, E. P.

B. A. C. Wang Z., H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 13 (2004).
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Simonyan, K.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” Comput. Sci. (2014).

Stelzer, E. H. K.

P. J. Keller, A. D. Schmidt, J. Wittbrodt, and E. H. K. Stelzer, “Reconstruction of Zebrafish Early Embryonic Development by Scanned Light Sheet Microscopy,” Science 322(5904), 1065–1069 (2008).
[Crossref] [PubMed]

Su, T.

A. Greenbaum, W. Luo, B. Khademhosseinieh, T. 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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Sü, S.

P. Vandewalle, S. Sü, and M. Vetterli, “A frequency domain approach to registration of aliased images with application to super-resolution,” EURASIP J. Adv. Signal Process. 2006, 1–14 (2006).

Sung, Y.

Tejani, A.

C. Ledig, Z. Wang, W. Shi, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, and A. Tejani, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” https://arxiv.org/abs/1609.04802 (2016).

Theis, L.

C. Ledig, Z. Wang, W. Shi, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, and A. Tejani, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” https://arxiv.org/abs/1609.04802 (2016).

Thiébaut, E.

Timofeeva, N.

G. Litjens, C. I. Sánchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen-van de Kaa, P. Bult, B. van Ginneken, and J. van der Laak, “Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis,” Sci. Rep. 6(1), 26286 (2016).
[Crossref] [PubMed]

Totz, J.

W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional,” Neural Netw. 2016, 1874–1883 (2016).

Trede, D.

Tseng, D.

Y. Rivenson, H. C. Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydin, Y. Zhang, Z. Gorocs, K. Liang, and D. Tseng, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2017).

van der Laak, J.

G. Litjens, C. I. Sánchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen-van de Kaa, P. Bult, B. van Ginneken, and J. van der Laak, “Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis,” Sci. Rep. 6(1), 26286 (2016).
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van Ginneken, B.

G. Litjens, C. I. Sánchez, N. Timofeeva, M. Hermsen, I. Nagtegaal, I. Kovacs, C. Hulsbergen-van de Kaa, P. Bult, B. van Ginneken, and J. van der Laak, “Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis,” Sci. Rep. 6(1), 26286 (2016).
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P. Vandewalle, S. Sü, and M. Vetterli, “A frequency domain approach to registration of aliased images with application to super-resolution,” EURASIP J. Adv. Signal Process. 2006, 1–14 (2006).

Vetterli, M.

P. Vandewalle, S. Sü, and M. Vetterli, “A frequency domain approach to registration of aliased images with application to super-resolution,” EURASIP J. Adv. Signal Process. 2006, 1–14 (2006).

Wang, H.

Y. Rivenson, H. C. Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydin, Y. Zhang, Z. Gorocs, K. Liang, and D. Tseng, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2017).

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

Wang, Z.

W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional,” Neural Netw. 2016, 1874–1883 (2016).

C. Ledig, Z. Wang, W. Shi, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, and A. Tejani, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” https://arxiv.org/abs/1609.04802 (2016).

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Y. Rivenson, H. C. Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydin, Y. Zhang, Z. Gorocs, K. Liang, and D. Tseng, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2017).

Weiss, L. E.

Wittbrodt, J.

P. J. Keller, A. D. Schmidt, J. Wittbrodt, and E. H. K. Stelzer, “Reconstruction of Zebrafish Early Embryonic Development by Scanned Light Sheet Microscopy,” Science 322(5904), 1065–1069 (2008).
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H. Zhang, X. Xie, C. Fang, Y. Yang, D. Jin, and P. Fei, “High-throughput, high-resolution Generated Adversarial Network Microscopy,” https://arxiv.org/abs/1801.07330 (2018).

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I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in International Conference on Neural Information Processing Systems(2014), pp. 2672–2680.

Xu, W.

W. Xu, M. H. Jericho, I. A. Meinertzhagen, and H. J. Kreuzer, “Digital in-line holography for biological applications,” Proc. Natl. Acad. Sci. U.S.A. 98(20), 11301–11305 (2001).
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G. Zheng, R. Horstmeyer, and C. Yang, “Wide-field, high-resolution Fourier ptychographic microscopy,” Nat. Photonics 7(9), 739–745 (2013).
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G. Zheng, S. A. Lee, Y. Antebi, M. B. Elowitz, and C. Yang, “The ePetri dish, an on-chip cell imaging platform based on subpixel perspective sweeping microscopy (SPSM),” Proc. Natl. Acad. Sci. U.S.A. 108(41), 16889–16894 (2011).
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G. Zheng, S. A. Lee, S. Yang, and C. Yang, “Sub-pixel resolving optofluidic microscope for on-chip cell imaging,” Lab Chip 10(22), 3125–3129 (2010).
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G. Zheng, S. A. Lee, S. Yang, and C. Yang, “Sub-pixel resolving optofluidic microscope for on-chip cell imaging,” Lab Chip 10(22), 3125–3129 (2010).
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H. Zhang, X. Xie, C. Fang, Y. Yang, D. Jin, and P. Fei, “High-throughput, high-resolution Generated Adversarial Network Microscopy,” https://arxiv.org/abs/1801.07330 (2018).

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H. Zhang, X. Xie, C. Fang, Y. Yang, D. Jin, and P. Fei, “High-throughput, high-resolution Generated Adversarial Network Microscopy,” https://arxiv.org/abs/1801.07330 (2018).

Zhang, Y.

Y. Rivenson, H. C. Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydin, Y. Zhang, Z. Gorocs, K. Liang, and D. Tseng, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2017).

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

W. Luo, Y. Zhang, A. Feizi, Z. Göröcs, and A. Ozcan, “Pixel super-resolution using wavelength scanning,” Light Sci. Appl. 5(4), e16060 (2016).
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W. Luo, A. Greenbaum, Y. Zhang, and A. Ozcan, “Synthetic aperture-based on-chip microscopy,” Light Sci. Appl. 4(3), e261 (2015).
[Crossref]

Zheng, G.

G. Zheng, R. Horstmeyer, and C. Yang, “Wide-field, high-resolution Fourier ptychographic microscopy,” Nat. Photonics 7(9), 739–745 (2013).
[Crossref] [PubMed]

G. Zheng, S. A. Lee, Y. Antebi, M. B. Elowitz, and C. Yang, “The ePetri dish, an on-chip cell imaging platform based on subpixel perspective sweeping microscopy (SPSM),” Proc. Natl. Acad. Sci. U.S.A. 108(41), 16889–16894 (2011).
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G. Zheng, S. A. Lee, S. Yang, and C. Yang, “Sub-pixel resolving optofluidic microscope for on-chip cell imaging,” Lab Chip 10(22), 3125–3129 (2010).
[Crossref] [PubMed]

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]

Zisserman, A.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” Comput. Sci. (2014).

ACS Photonics (1)

Y. Rivenson, H. C. Koydemir, H. Wang, Z. Wei, Z. Ren, H. Gunaydin, Y. Zhang, Z. Gorocs, K. Liang, and D. Tseng, “Deep learning enhanced mobile-phone microscopy,” ACS Photonics 5, 2354–2364 (2017).

Biomed. Opt. Express (1)

EURASIP J. Adv. Signal Process. (1)

P. Vandewalle, S. Sü, and M. Vetterli, “A frequency domain approach to registration of aliased images with application to super-resolution,” EURASIP J. Adv. Signal Process. 2006, 1–14 (2006).

IEEE Trans. Biomed. Eng. (2)

K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
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K. Imfeld, S. Neukom, A. Maccione, Y. Bornat, S. Martinoia, P. A. Farine, M. Koudelka-Hep, and L. Berdondini, “Large-scale, high-resolution data acquisition system for extracellular recording of electrophysiological activity,” IEEE Trans. Biomed. Eng. 55(8), 2064–2073 (2008).
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B. A. C. Wang Z., H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Trans. Image Process. 13(4), 13 (2004).
[Crossref]

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16(8), 2080–2095 (2007).
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M. Brown and D. G. Lowe, “Automatic panoramic image stitching using invariant features,” Int. J. Comput. Vis. 74(1), 59–73 (2007).
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K. Becker, N. Jährling, E. R. Kramer, F. Schnorrer, and H. U. Dodt, “Ultramicroscopy: 3D reconstruction of large microscopical specimens,” J. Biophotonics 1(1), 36–42 (2008).
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Lab Chip (1)

G. Zheng, S. A. Lee, S. Yang, and C. Yang, “Sub-pixel resolving optofluidic microscope for on-chip cell imaging,” Lab Chip 10(22), 3125–3129 (2010).
[Crossref] [PubMed]

Light Sci. Appl. (2)

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, 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]

Nat. Photonics (1)

G. Zheng, R. Horstmeyer, and C. Yang, “Wide-field, high-resolution Fourier ptychographic microscopy,” Nat. Photonics 7(9), 739–745 (2013).
[Crossref] [PubMed]

Neural Netw. (1)

W. Shi, J. Caballero, F. Huszar, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, “Real-time single image and video super-resolution using an efficient sub-pixel convolutional,” Neural Netw. 2016, 1874–1883 (2016).

Opt. Express (1)

Opt. Lett. (3)

Optica (2)

Proc. Natl. Acad. Sci. U.S.A. (3)

W. Xu, M. H. Jericho, I. A. Meinertzhagen, and H. J. Kreuzer, “Digital in-line holography for biological applications,” Proc. Natl. Acad. Sci. U.S.A. 98(20), 11301–11305 (2001).
[Crossref] [PubMed]

G. Zheng, S. A. Lee, Y. Antebi, M. B. Elowitz, and C. Yang, “The ePetri dish, an on-chip cell imaging platform based on subpixel perspective sweeping microscopy (SPSM),” Proc. Natl. Acad. Sci. U.S.A. 108(41), 16889–16894 (2011).
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M. G. Gustafsson, “Nonlinear structured-illumination microscopy: wide-field fluorescence imaging with theoretically unlimited resolution,” Proc. Natl. Acad. Sci. U.S.A. 102(37), 13081–13086 (2005).
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A. Greenbaum, W. Luo, B. Khademhosseinieh, T. 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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Science (1)

P. J. Keller, A. D. Schmidt, J. Wittbrodt, and E. H. K. Stelzer, “Reconstruction of Zebrafish Early Embryonic Development by Scanned Light Sheet Microscopy,” Science 322(5904), 1065–1069 (2008).
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G. Hilgen, S. Pirmoradian, A. Maccione, L. Berdondini, M. H. Hennig, and E. Sernagor, “High Resolution Large-Scale Recordings of Light Responses from Ganglion Cells in the Developing Mouse Retina,” in FASEB Conference on Retinal Neurobiology and Visual Processing(2014).

C. Ledig, Z. Wang, W. Shi, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, and A. Tejani, “Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,” https://arxiv.org/abs/1609.04802 (2016).

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K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” Comput. Sci. (2014).

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N. Boyd, E. Jonas, and H. B. B. Recht, “DeepLoco: fast 3D localization microscopy using neural networks,” http://bioRxiv236463 (2018)

Y. R. Hongda Wang, Y. Jin, Z. Wei, R. Gao, L. A. B. Harun, Günaydın, and A. Ozcan, “Deep learning achieves super-resolution in fluorescence microscopy,” https://bioRxiv309641 (2018).

H. Zhang, X. Xie, C. Fang, Y. Yang, D. Jin, and P. Fei, “High-throughput, high-resolution Generated Adversarial Network Microscopy,” https://arxiv.org/abs/1801.07330 (2018).

I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in International Conference on Neural Information Processing Systems(2014), pp. 2672–2680.

J. Yang, J. Wright, T. Huang, and Y. Ma, “Image super-resolution as sparse representation of raw image patches,” in IEEE Conference on Computer Vision and Pattern Recognition (2008), pp. 1–8.

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M. G. Ertosun and D. L. Rubin, “Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks,” AMIA Symposium2015, 1899 (2015).

A. Cruzroa, A. Basavanhally, H. Gilmore, M. Feldman, S. Ganesan, N. Shih, J. Tomaszewski, and A. Madabhushi, “Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks,” Proceedings of SPIE - The International Society for Optical Engineering 9041, 139–144 (2014).

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Color Image Denoising via Sparse 3D Collaborative Filtering with Grouping Constraint in Luminance-Chrominance Space,” in IEEE International Conference on Image Processing(2007), pp. 313–316.
[Crossref]

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Figures (10)

Fig. 1
Fig. 1 Principle of RFGANM procedure. A modified GAN is used to iteratively learn the microscopy data till the goal of high-quality output is reached. (a) The training process of the RFGAN. (b) The RFGANM reconstruction procedure, using a large-FOV, low-resolution measurement of new sample.
Fig. 2
Fig. 2 Generation of simulated LR inputs via a degradation model. (a) Denoised 4 × LR measurement used for finding the optimized blurring kernel of microscope. (b) The blurred and down-sampled images from 20 × measurement, by different sizes of blurring kernel (Sigma value). (c) Blurred image with optimal blurring is processed with different additive noises and compared to the realistic 4 × measurement. The best matched level of noise is found as a result. (e-g) A 4 × measurement is subtracted by the optimized 4 × simulation to verify the efficacy of the model.
Fig. 3
Fig. 3 Network outputs with different degrading parameters. (a) A 10 × measurement of a human stomach tissue slice. (b1-b4) LR simulations degraded from (a), with different parameters applied. Sigma is the standard deviation of the Gaussian blur kernel and variance denotes the variance of the white noise distribution. (c1-c4) After the network being trained by different simulation data, it correspondingly generates different reconstruction results that are recovered from the same LR measurement (d).
Fig. 4
Fig. 4 The architecture of GAN. (a) The architecture of the generator. Conv and ResNet is the abbreviation of Convolutional layer and Residual network block. The parameters of the convolutional layers is given in the format ”k-s-n”, where k is the kernel size, s is the strides and n is the number of feature maps (i.e. the output channels of the layer). The depth of each convolutional layer roughly denotes the number of its feature maps, and the lateral dimensions denotes the size of its input. Totally, there are 16 residual blocks in the generator. (b) The architecture of the discriminator. Each convolutional layer in the discriminator is the combination of a convolution layer, a batch normalization operation and a ReLU activation function.
Fig. 5
Fig. 5 Resolution characterization of GAN using USAF target. (a) The initial implementation of RFGANM, including HR training data acquisition, GAN network training, LR image acquisition of the sample, and HR reconstruction. (b) Image of the USAF resolution target taken under 2 × low-magnification of a macro-zoom microscope. (c1-d1) Magnified views of the raw image, with a pixel size of 2.13 μm. (c2-d2) Reconstructions of a well-trained GAN-generator, with an enhancement factor of 5 (reconstructed pixel size, 0.425 μm). (c3-d3) Corresponding high-resolution images taken under 10 × magnification. (e) Intensity plot of linecuts (shown in (d1-d3)) for each method, indicating that RFGANM prototype provides substantively improved contrast and resolution (FWHM ~1.7 μm) that potentially enable subcellular-level imaging across a centimeter large scale.
Fig. 6
Fig. 6 Dual-color fluorescence imaging via RFGANM. (a) RFGANM-reconstruction of a wide-FOV fluorescence image of BPAE cells specifically labelled with DAPI and Alexa Fluor-488, at nucleus and skeletons, respectively. (a) and (b) show the imaging FOV of 4 × (which RFGANM inherits) and 20 × objectives (high-resolution conventional microscopy), respectively, by using a sCMOS camera (sensor area 1.33 × 1.33 cm). (c1), (d1), (e) and (f) High-resolution views of the selected regions (blue) in (a). (c2) and (d2) High resolution iamges taken under a conventional wide-field fluorescence microscope with a 20 × /0.45 objective. (c3) and (d3) Low resolution inputs taken under 4 × /0.1 objective. (c4) and (d4) The deconvolution results of (c3) and (d3) respectively.
Fig. 7
Fig. 7 Gigapixel color imaging of prostate tissue slides. (a) and (f) RFGANM color images of normal prostate histology slide and prostate cancer pathology slide, respectively. The achieved effective SBP here is ~0.1 gigapixels. (b-e) Vignette high-resolution views of the image in (a). (g-j) Vignette high-resolution views of the image in (f) .(c1) and (i1) The 2.5 × input images of RFGANM to get (c) and (i); (c2) and (i2) Images taken by an Olympus macro-zoom microscope under 10 × magnifications; (c3) and (i3) Deconvolution results of 2.5 × inputs (c1) and (i1), respectively, for comparison with RFGANM results.
Fig. 8
Fig. 8 GAN-based light-sheet fluorescence microscopy. (a) Schematic of light-sheet imaging geometry for optical sectioning in the deep of intact organ. (b) High-contrast plane images of the mouse brain. (c) A 1mm-thick transverse slice of the whole brain, which is the MIP of 200 consecutively illuminated planes. The image was then super-resolved with 4 times enhancement, and compared to raw 1.6 × (d1) and 6.4 × measurements (d3). (e-h) Magnified reconstruction views of selective regions of cortex, telencephalon, hippocampus, and cerebellum.
Fig. 9
Fig. 9 Validating the robustness of RFGANM. (a1) and (a2) Wide-FOV reconstruction images of a prostate cancer slide using homogenous-data-trained (prostate cancer images) network and heterogenous-data-trained (healthy prostate images) network. (b1-d1) Vignette high-resolution views of the image in (a1). (b2-d2) Vignette high-resolution views of the (a2). (b3-d3) Images taken by a macro-zoom microscope (mv10 × ) under 2.5 × magnification. (b4-d4) Images taken under 10 × magnification.
Fig. 10
Fig. 10 Biomedical quantitative analysis based on RFGANM images. (a) The encephalic regions division of the whole mouse brain section. 4 main encephalic regions are segmented as Cortex, Hippocampus, Diencephalon, Pons and Medulla. (b1-b4) Examples of identified and counted neurons in 4 regions, respectively. (c) The calculated cellular populations at different brain regions of (a). The counting results of RFGAM are consistent with those from HR measurements. (d and f) The selected gland areas from RFGANM images of a prostate tissue and a prostate cancer slide, respectively, for cell nuclei counting. (e and g) Examples of identified and counted cell nuclei. (h and i) The counting results of (d) and (f), respectively.

Tables (1)

Tables Icon

Table 1 PSNR and SSIM between the GAN-reconstructed results and the realistic HR measurements

Equations (6)

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I m =D(KI)+N
l G = 1 N n=1 N ( l MSE G ( G θ G ( Δ( I n HR ) ), I n HR )+ 20 6 l feat G ( G θ G ( Δ( I n HR ) ), I n HR )+ 10 3 l adv G ( G θ G ( Δ( I n HR ) ) ) )
l MSE G ( G θ G ( Δ( I n HR ) ), I n HR )= 1 r 2 WH x=1 rW y=1 rH ( I x,y HR G θ G ( Δ( I n HR ) ) x,y ) 2
l feat/j G ( G θ G ( Δ( I n HR ) ), I n HR )= 1 W j H j x=1 W j y=1 H j ( ϕ j ( I HR ) x,y ϕ j ( G θ G ( Δ( I n HR ) ) ) x,y ) 2
l adv G ( G θ G ( Δ( I n HR ) ) )=log D θ D ( G θ G ( Δ( I n HR ) ) )
l D = 1 N n=1 N ( log( D θ D ( I n HR ) )log( 1 D θ D ( G θ G ( Δ( I n HR ) ) ) ) )

Metrics