C. Ledig, L. Theis, F. Huczar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Zehan Wang, and Wenshe Shi, “Photo-realistic single image super-resolution using a Generative Adversarial Network,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), pp. 4681–4690.

M. S. Advani and A. M. Saxe, “High-dimensional dynamics of generalization error in neural networks,” arXiv preprint arXiv:1710.03667 (2017).

C. Ledig, L. Theis, F. Huczar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Zehan Wang, and Wenshe Shi, “Photo-realistic single image super-resolution using a Generative Adversarial Network,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), pp. 4681–4690.

A. Goy, G. Rughoobur, Shuai Li, K. Arthur, A. Akinwande, and G. Barbastathis, “High-resolution limited-angle phase tomography of dense layered objects using deep neural networks,” Proc. Nat. Acad. Sci. ((accepted) 2019).

J. Johnson, A. Alahi, and Li Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European Conference on Computer Vision (ECCV) / Lecture Notes on Computer Science, vol. 9906B. Leide, J. Matas, N. Sebe, and M. Welling, eds. (2016), pp. 694–711.

M. Deng, A. Goy, S. Li, K. Arthur, and G. Barbastathis, “Probing shallower: perceptual loss trained phase extraction neural network (plt-phenn) for artifact-free reconstruction at low photon budget,” Opt. Express 28(2), 2511–2535 (2020).

[Crossref]

A. Goy, K. Arthur, Shuai Li, and G. Barbastathis, “Low photon count phase retrieval using deep learning,” Phys. Rev. Lett. 121(24), 243902 (2018).

[Crossref]

A. Goy, G. Rughoobur, Shuai Li, K. Arthur, A. Akinwande, and G. Barbastathis, “High-resolution limited-angle phase tomography of dense layered objects using deep neural networks,” Proc. Nat. Acad. Sci. ((accepted) 2019).

M. Deng, S. Li, A. Goy, I. Kang, and G. Barbastathis, “Learning to synthesize: Robust phase retrieval at low photon counts,” Light: Sci. Appl. 9(1), 36 (2020).

[Crossref]

M. Deng, A. Goy, S. Li, K. Arthur, and G. Barbastathis, “Probing shallower: perceptual loss trained phase extraction neural network (plt-phenn) for artifact-free reconstruction at low photon budget,” Opt. Express 28(2), 2511–2535 (2020).

[Crossref]

S. Li and G. Barbastathis, “Spectral pre-modulation of training examples enhances the spatial resolution of the phase extraction neural network (PhENN),” Opt. Express 26(22), 29340–29352 (2018).

[Crossref]

A. Goy, K. Arthur, Shuai Li, and G. Barbastathis, “Low photon count phase retrieval using deep learning,” Phys. Rev. Lett. 121(24), 243902 (2018).

[Crossref]

S. Li, M. Deng, J. Lee, A. Sinha, and G. Barbastathis, “Imaging through glass diffusers using densely connected convolutional networks,” Optica 5(7), 803–813 (2018).

[Crossref]

A. Sinha, Justin Lee, Shuai Li, and G. Barbastathis, “Lensless computational imaging through deep learning,” Optica 4(9), 1117–1125 (2017).

[Crossref]

M. Deng, S. Li, and G. Barbastathis, “Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery,” arXiv preprint arXiv:1811.07945 (2018).

A. Goy, G. Rughoobur, Shuai Li, K. Arthur, A. Akinwande, and G. Barbastathis, “High-resolution limited-angle phase tomography of dense layered objects using deep neural networks,” Proc. Nat. Acad. Sci. ((accepted) 2019).

G. Barbastathis, A. Ozcan, and Guohai Situ, “On the use of deep learning for computational imaging,” Optica (2019).

S. Li, G. Barbastathis, and A. Goy, “Analysis of phase-extraction neural network (phenn) performance for lensless quantitative phase imaging,” in Quantitative Phase Imaging V, vol. 10887 (International Society for Optics and Photonics, 2019), p. 108870T.

C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning requires rethinking generalization,” arXiv preprint arXiv:1611.03530 (2016).

H. Wang, Y. Rivenson, Z. Wei, H. Gunaydin, L. Bentolila, and A. Ozcan, “Deep learning achieves super-resolution in fluorescence microscopy,” bioRxiv, https://doi.org/10.1101/309641 (2018).

G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database forstudying face recognition in unconstrained environments,” in Technical Report, University of Massachusetts, (2007).

B. Neyshabur, Z. Li, S. Bhojanapalli, Y. LeCun, and N. Srebro, “Towards understanding the role of over-parametrization in generalization of neural networks,” arXiv preprint arXiv:1805.12076 (2018).

B. Neyshabur, S. Bhojanapalli, and N. Srebro, “A pac-bayesian approach to spectrally-normalized margin bounds for neural networks,” arXiv preprint arXiv:1707.09564 (2017).

B. Neyshabur, S. Bhojanapalli, D. McAllester, and N. Srebro, “Exploring generalization in deep learning,” in Advances in Neural Information Processing Systems, (2017), pp. 5947–5956.

M. R. Kellman, E. Bostan, N. A. Repina, and L. Waller, “Physics-based learned design: Optimized coded-illumination for quantitative phase imaging,” IEEE Trans. Comput. Imaging 5(3), 344–353 (2019).

[Crossref]

Y. LeCun, C. Cortes, and C. J. Burges, “MNIST handwritten digit database,” AT&T Labs [Online]. Available: http://yann.lecun.com/exdb/mnist 2 (2010).

C. Ledig, L. Theis, F. Huczar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Zehan Wang, and Wenshe Shi, “Photo-realistic single image super-resolution using a Generative Adversarial Network,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), pp. 4681–4690.

Y. LeCun, C. Cortes, and C. J. Burges, “MNIST handwritten digit database,” AT&T Labs [Online]. Available: http://yann.lecun.com/exdb/mnist 2 (2010).

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C. Ledig, L. Theis, F. Huczar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Zehan Wang, and Wenshe Shi, “Photo-realistic single image super-resolution using a Generative Adversarial Network,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), pp. 4681–4690.

G. K. Matsopoulos, N. A. Mouravliansky, K. K. Delibasis, and K. S. Nikita, “Automatic retinal image registration scheme using global optimization techniques,” IEEE Trans. Inform. Technol. Biomed. 3(1), 47–60 (1999).

[Crossref]

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database, in 2009 IEEE conference on computer vision and pattern recognition (Ieee, 2009), pp. 248–255.

M. Deng, S. Li, A. Goy, I. Kang, and G. Barbastathis, “Learning to synthesize: Robust phase retrieval at low photon counts,” Light: Sci. Appl. 9(1), 36 (2020).

[Crossref]

M. Deng, A. Goy, S. Li, K. Arthur, and G. Barbastathis, “Probing shallower: perceptual loss trained phase extraction neural network (plt-phenn) for artifact-free reconstruction at low photon budget,” Opt. Express 28(2), 2511–2535 (2020).

[Crossref]

S. Li, M. Deng, J. Lee, A. Sinha, and G. Barbastathis, “Imaging through glass diffusers using densely connected convolutional networks,” Optica 5(7), 803–813 (2018).

[Crossref]

M. Deng, S. Li, and G. Barbastathis, “Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery,” arXiv preprint arXiv:1811.07945 (2018).

C. Dong, C. Loy, K. He, and X. Tang, “Learning a deep convolutional neural network for image super-resolution,” in European Conference on Computer Vision (ECCV) / Lecture Notes on Computer Science Part IV, vol. 8692, (2014), pp. 184–199.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database, in 2009 IEEE conference on computer vision and pattern recognition (Ieee, 2009), pp. 248–255.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database, in 2009 IEEE conference on computer vision and pattern recognition (Ieee, 2009), pp. 248–255.

J. Johnson, A. Alahi, and Li Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European Conference on Computer Vision (ECCV) / Lecture Notes on Computer Science, vol. 9906B. Leide, J. Matas, N. Sebe, and M. Welling, eds. (2016), pp. 694–711.

D. Jakubovitz, R. Giryes, and M. R. Rodrigues, “Generalization error in deep learning,” in Compressed Sensing and Its Applications (Springer, 2019), pp. 153–193.

M. Deng, A. Goy, S. Li, K. Arthur, and G. Barbastathis, “Probing shallower: perceptual loss trained phase extraction neural network (plt-phenn) for artifact-free reconstruction at low photon budget,” Opt. Express 28(2), 2511–2535 (2020).

[Crossref]

M. Deng, S. Li, A. Goy, I. Kang, and G. Barbastathis, “Learning to synthesize: Robust phase retrieval at low photon counts,” Light: Sci. Appl. 9(1), 36 (2020).

[Crossref]

A. Goy, K. Arthur, Shuai Li, and G. Barbastathis, “Low photon count phase retrieval using deep learning,” Phys. Rev. Lett. 121(24), 243902 (2018).

[Crossref]

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Optical tomographic image reconstruction based on beam propagation and sparse regularization,” IEEE Trans. Comput. Imaging 2(1), 59–70 (2016).

[Crossref]

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Learning approach to optical tomography,” Optica 2(6), 517–522 (2015).

[Crossref]

A. Goy, G. Rughoobur, Shuai Li, K. Arthur, A. Akinwande, and G. Barbastathis, “High-resolution limited-angle phase tomography of dense layered objects using deep neural networks,” Proc. Nat. Acad. Sci. ((accepted) 2019).

S. Li, G. Barbastathis, and A. Goy, “Analysis of phase-extraction neural network (phenn) performance for lensless quantitative phase imaging,” in Quantitative Phase Imaging V, vol. 10887 (International Society for Optics and Photonics, 2019), p. 108870T.

Y. Wu, Y. Rivenson, Y. Zhang, Z. Wei, H. Gunaydin, X. Lin, and A. Ozcan, “Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery,” Optica 5(6), 704–710 (2018).

[Crossref]

Y. Rivenson, Z. Gorocs, H. Gunaydin, Yibo Zhang, Hongda Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4(11), 1437–1443 (2017).

[Crossref]

H. Wang, Y. Rivenson, Z. Wei, H. Gunaydin, L. Bentolila, and A. Ozcan, “Deep learning achieves super-resolution in fluorescence microscopy,” bioRxiv, https://doi.org/10.1101/309641 (2018).

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]

C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning requires rethinking generalization,” arXiv preprint arXiv:1611.03530 (2016).

C. Dong, C. Loy, K. He, and X. Tang, “Learning a deep convolutional neural network for image super-resolution,” in European Conference on Computer Vision (ECCV) / Lecture Notes on Computer Science Part IV, vol. 8692, (2014), pp. 184–199.

G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database forstudying face recognition in unconstrained environments,” in Technical Report, University of Massachusetts, (2007).

C. Ledig, L. Theis, F. Huczar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Zehan Wang, and Wenshe Shi, “Photo-realistic single image super-resolution using a Generative Adversarial Network,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), pp. 4681–4690.

V. K. Ingle and J. G. Proakis, Digital signal processing using matlab: a problem solving companion (Cengage Learning, 2016).

D. Jakubovitz, R. Giryes, and M. R. Rodrigues, “Generalization error in deep learning,” in Compressed Sensing and Its Applications (Springer, 2019), pp. 153–193.

M. T. McCann, K. H. Jin, and M. Unser, “Convolutional neural networks for inverse problems in imaging: A review,” IEEE Signal Process. Mag. 34(6), 85–95 (2017).

[Crossref]

J. Johnson, A. Alahi, and Li Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European Conference on Computer Vision (ECCV) / Lecture Notes on Computer Science, vol. 9906B. Leide, J. Matas, N. Sebe, and M. Welling, eds. (2016), pp. 694–711.

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Optical tomographic image reconstruction based on beam propagation and sparse regularization,” IEEE Trans. Comput. Imaging 2(1), 59–70 (2016).

[Crossref]

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Learning approach to optical tomography,” Optica 2(6), 517–522 (2015).

[Crossref]

M. Deng, S. Li, A. Goy, I. Kang, and G. Barbastathis, “Learning to synthesize: Robust phase retrieval at low photon counts,” Light: Sci. Appl. 9(1), 36 (2020).

[Crossref]

M. R. Kellman, E. Bostan, N. A. Repina, and L. Waller, “Physics-based learned design: Optimized coded-illumination for quantitative phase imaging,” IEEE Trans. Comput. Imaging 5(3), 344–353 (2019).

[Crossref]

D. P. Kingma and J. Lei Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations (ICLR), (2015).

G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database forstudying face recognition in unconstrained environments,” in Technical Report, University of Massachusetts, (2007).

B. Neyshabur, Z. Li, S. Bhojanapalli, Y. LeCun, and N. Srebro, “Towards understanding the role of over-parametrization in generalization of neural networks,” arXiv preprint arXiv:1805.12076 (2018).

Y. LeCun, C. Cortes, and C. J. Burges, “MNIST handwritten digit database,” AT&T Labs [Online]. Available: http://yann.lecun.com/exdb/mnist 2 (2010).

C. Ledig, L. Theis, F. Huczar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Zehan Wang, and Wenshe Shi, “Photo-realistic single image super-resolution using a Generative Adversarial Network,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), pp. 4681–4690.

D. P. Kingma and J. Lei Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations (ICLR), (2015).

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database, in 2009 IEEE conference on computer vision and pattern recognition (Ieee, 2009), pp. 248–255.

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database, in 2009 IEEE conference on computer vision and pattern recognition (Ieee, 2009), pp. 248–255.

M. Deng, S. Li, A. Goy, I. Kang, and G. Barbastathis, “Learning to synthesize: Robust phase retrieval at low photon counts,” Light: Sci. Appl. 9(1), 36 (2020).

[Crossref]

M. Deng, A. Goy, S. Li, K. Arthur, and G. Barbastathis, “Probing shallower: perceptual loss trained phase extraction neural network (plt-phenn) for artifact-free reconstruction at low photon budget,” Opt. Express 28(2), 2511–2535 (2020).

[Crossref]

S. Li and G. Barbastathis, “Spectral pre-modulation of training examples enhances the spatial resolution of the phase extraction neural network (PhENN),” Opt. Express 26(22), 29340–29352 (2018).

[Crossref]

S. Li, M. Deng, J. Lee, A. Sinha, and G. Barbastathis, “Imaging through glass diffusers using densely connected convolutional networks,” Optica 5(7), 803–813 (2018).

[Crossref]

M. Deng, S. Li, and G. Barbastathis, “Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery,” arXiv preprint arXiv:1811.07945 (2018).

S. Li, G. Barbastathis, and A. Goy, “Analysis of phase-extraction neural network (phenn) performance for lensless quantitative phase imaging,” in Quantitative Phase Imaging V, vol. 10887 (International Society for Optics and Photonics, 2019), p. 108870T.

S. Li, “Computational imaging through deep learning,” Ph.D. thesis, MIT (2019).

A. Goy, K. Arthur, Shuai Li, and G. Barbastathis, “Low photon count phase retrieval using deep learning,” Phys. Rev. Lett. 121(24), 243902 (2018).

[Crossref]

A. Sinha, Justin Lee, Shuai Li, and G. Barbastathis, “Lensless computational imaging through deep learning,” Optica 4(9), 1117–1125 (2017).

[Crossref]

A. Goy, G. Rughoobur, Shuai Li, K. Arthur, A. Akinwande, and G. Barbastathis, “High-resolution limited-angle phase tomography of dense layered objects using deep neural networks,” Proc. Nat. Acad. Sci. ((accepted) 2019).

Y. Li, Y. Xue, and L. Tian, “Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media,” Optica 5(10), 1181–1190 (2018).

[Crossref]

T. Nguyen, Y. Xue, Y. Li, L. Tian, and G. Nehmetallah, “Deep learning approach for fourier ptychography microscopy,” Opt. Express 26(20), 26470–26484 (2018).

[Crossref]

B. Neyshabur, Z. Li, S. Bhojanapalli, Y. LeCun, and N. Srebro, “Towards understanding the role of over-parametrization in generalization of neural networks,” arXiv preprint arXiv:1805.12076 (2018).

C. Dong, C. Loy, K. He, and X. Tang, “Learning a deep convolutional neural network for image super-resolution,” in European Conference on Computer Vision (ECCV) / Lecture Notes on Computer Science Part IV, vol. 8692, (2014), pp. 184–199.

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Performance of autofocus capability of deep convolutional neural networks in digital holographic microscopy,” in Digital Holography and Three-Dimensional Imaging (OSA, 2017), p. W2A.5.

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[Crossref]

G. K. Matsopoulos, N. A. Mouravliansky, K. K. Delibasis, and K. S. Nikita, “Automatic retinal image registration scheme using global optimization techniques,” IEEE Trans. Inform. Technol. Biomed. 3(1), 47–60 (1999).

[Crossref]

G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database forstudying face recognition in unconstrained environments,” in Technical Report, University of Massachusetts, (2007).

B. Neyshabur, S. Bhojanapalli, D. McAllester, and N. Srebro, “Exploring generalization in deep learning,” in Advances in Neural Information Processing Systems, (2017), pp. 5947–5956.

M. T. McCann, K. H. Jin, and M. Unser, “Convolutional neural networks for inverse problems in imaging: A review,” IEEE Signal Process. Mag. 34(6), 85–95 (2017).

[Crossref]

J. A. Nelder and R. Mead, “A simplex method for function minimization,” The computer journal 7(4), 308–313 (1965).

[Crossref]

G. K. Matsopoulos, N. A. Mouravliansky, K. K. Delibasis, and K. S. Nikita, “Automatic retinal image registration scheme using global optimization techniques,” IEEE Trans. Inform. Technol. Biomed. 3(1), 47–60 (1999).

[Crossref]

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Performance of autofocus capability of deep convolutional neural networks in digital holographic microscopy,” in Digital Holography and Three-Dimensional Imaging (OSA, 2017), p. W2A.5.

J. A. Nelder and R. Mead, “A simplex method for function minimization,” The computer journal 7(4), 308–313 (1965).

[Crossref]

B. Neyshabur, S. Bhojanapalli, and N. Srebro, “A pac-bayesian approach to spectrally-normalized margin bounds for neural networks,” arXiv preprint arXiv:1707.09564 (2017).

B. Neyshabur, Z. Li, S. Bhojanapalli, Y. LeCun, and N. Srebro, “Towards understanding the role of over-parametrization in generalization of neural networks,” arXiv preprint arXiv:1805.12076 (2018).

B. Neyshabur, S. Bhojanapalli, D. McAllester, and N. Srebro, “Exploring generalization in deep learning,” in Advances in Neural Information Processing Systems, (2017), pp. 5947–5956.

G. K. Matsopoulos, N. A. Mouravliansky, K. K. Delibasis, and K. S. Nikita, “Automatic retinal image registration scheme using global optimization techniques,” IEEE Trans. Inform. Technol. Biomed. 3(1), 47–60 (1999).

[Crossref]

Y. Wu, Y. Rivenson, Y. Zhang, Z. Wei, H. Gunaydin, X. Lin, and A. Ozcan, “Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery,” Optica 5(6), 704–710 (2018).

[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]

Y. Rivenson, Z. Gorocs, H. Gunaydin, Yibo Zhang, Hongda Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4(11), 1437–1443 (2017).

[Crossref]

H. Wang, Y. Rivenson, Z. Wei, H. Gunaydin, L. Bentolila, and A. Ozcan, “Deep learning achieves super-resolution in fluorescence microscopy,” bioRxiv, https://doi.org/10.1101/309641 (2018).

G. Barbastathis, A. Ozcan, and Guohai Situ, “On the use of deep learning for computational imaging,” Optica (2019).

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Optical tomographic image reconstruction based on beam propagation and sparse regularization,” IEEE Trans. Comput. Imaging 2(1), 59–70 (2016).

[Crossref]

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Learning approach to optical tomography,” Optica 2(6), 517–522 (2015).

[Crossref]

T. Pitkäaho, A. Manninen, and T. J. Naughton, “Performance of autofocus capability of deep convolutional neural networks in digital holographic microscopy,” in Digital Holography and Three-Dimensional Imaging (OSA, 2017), p. W2A.5.

V. K. Ingle and J. G. Proakis, Digital signal processing using matlab: a problem solving companion (Cengage Learning, 2016).

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Optical tomographic image reconstruction based on beam propagation and sparse regularization,” IEEE Trans. Comput. Imaging 2(1), 59–70 (2016).

[Crossref]

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Learning approach to optical tomography,” Optica 2(6), 517–522 (2015).

[Crossref]

C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning requires rethinking generalization,” arXiv preprint arXiv:1611.03530 (2016).

M. R. Kellman, E. Bostan, N. A. Repina, and L. Waller, “Physics-based learned design: Optimized coded-illumination for quantitative phase imaging,” IEEE Trans. Comput. Imaging 5(3), 344–353 (2019).

[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]

Y. Wu, Y. Rivenson, Y. Zhang, Z. Wei, H. Gunaydin, X. Lin, and A. Ozcan, “Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery,” Optica 5(6), 704–710 (2018).

[Crossref]

Y. Rivenson, Z. Gorocs, H. Gunaydin, Yibo Zhang, Hongda Wang, and A. Ozcan, “Deep learning microscopy,” Optica 4(11), 1437–1443 (2017).

[Crossref]

H. Wang, Y. Rivenson, Z. Wei, H. Gunaydin, L. Bentolila, and A. Ozcan, “Deep learning achieves super-resolution in fluorescence microscopy,” bioRxiv, https://doi.org/10.1101/309641 (2018).

D. Jakubovitz, R. Giryes, and M. R. Rodrigues, “Generalization error in deep learning,” in Compressed Sensing and Its Applications (Springer, 2019), pp. 153–193.

A. Goy, G. Rughoobur, Shuai Li, K. Arthur, A. Akinwande, and G. Barbastathis, “High-resolution limited-angle phase tomography of dense layered objects using deep neural networks,” Proc. Nat. Acad. Sci. ((accepted) 2019).

M. S. Advani and A. M. Saxe, “High-dimensional dynamics of generalization error in neural networks,” arXiv preprint arXiv:1710.03667 (2017).

C. E. Shannon, “A mathematical theory of communication,” Bell Syst. Tech. J. 27(3), 379–423 (1948).

[Crossref]

C. Ledig, L. Theis, F. Huczar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Zehan Wang, and Wenshe Shi, “Photo-realistic single image super-resolution using a Generative Adversarial Network,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (2017), pp. 4681–4690.

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Optical tomographic image reconstruction based on beam propagation and sparse regularization,” IEEE Trans. Comput. Imaging 2(1), 59–70 (2016).

[Crossref]

U. S. Kamilov, I. N. Papadopoulos, M. H. Shoreh, A. Goy, C. Vonesch, M. Unser, and D. Psaltis, “Learning approach to optical tomography,” Optica 2(6), 517–522 (2015).

[Crossref]

S. Li, M. Deng, J. Lee, A. Sinha, and G. Barbastathis, “Imaging through glass diffusers using densely connected convolutional networks,” Optica 5(7), 803–813 (2018).

[Crossref]

A. Sinha, Justin Lee, Shuai Li, and G. Barbastathis, “Lensless computational imaging through deep learning,” Optica 4(9), 1117–1125 (2017).

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