[1] Barbastathis, G., Ozcan, A. & Situ, G. On the use of deep learning for computational imaging. Optica 6, 921–943 (2019). doi: 10.1364/OPTICA.6.000921
[2] Kamilov, U. S. et al. Learning approach to optical tomography. Optica 2, 517–522 (2015). doi: 10.1364/OPTICA.2.000517
[3] Lyu, M. et al. Deep-learning-based ghost imaging. Sci. Rep. 7, 17865 (2017). doi: 10.1038/s41598-017-18171-7
[4] Wang, F. et al. Learning from simulation: an end-to-end deep-learning approach for computational ghost imaging. Opt. Express 27, 25560–25572 (2019). doi: 10.1364/OE.27.025560
[5] Ren, Z. B., Xu, Z. M. & Lam, E. Y. Learning-based nonparametric autofocusing for digital holography. Optica 5, 337–344 (2018). doi: 10.1364/OPTICA.5.000337
[6] Wang, H., Lyu, M. & Situ, G. eHoloNet: a learning-based end-to-end approach for in-line digital holographic reconstruction. Opt. Express 26, 22603–22614 (2018).
[7] Rivenson, Y. et al. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light Sci. Appl. 7, 17141 (2018). doi: 10.1038/lsa.2017.141
[8] Lyu, M. et al. Learning-based lensless imaging through optically thick scattering media. Adv. Photonics 1, 036002 (2019). doi: 10.1117/1.AP.1.3.036002
[9] Li, Y. Z., Xue, Y. J. & Tian, L. Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media. Optica 5, 1181–1190 (2018). doi: 10.1364/OPTICA.5.001181
[10] Li, S. et al. Imaging through glass diffusers using densely connected convolutional networks. Optica 5, 803–813 (2018). doi: 10.1364/OPTICA.5.000803
[11] Wu, G. et al. Artificial neural network approaches for fluorescence lifetime imaging techniques. Opt. Lett. 41, 2561–2564 (2016). doi: 10.1364/OL.41.002561
[12] Goy, A. et al. Low photon count phase retrieval using deep learning. Phys. Rev. Lett. 121, 243902 (2018). doi: 10.1103/PhysRevLett.121.243902
[13] Sinha, A. et al. Lensless computational imaging through deep learning. Optica 4, 1117–1125 (2017). doi: 10.1364/OPTICA.4.001117
[14] Li, X. et al. Quantitative phase imaging via a cGAN network with dual intensity images captured under centrosymmetric illumination. Opt. Lett. 44, 2879–2882 (2019). doi: 10.1364/OL.44.002879
[15] Xue, Y. J. et al. Reliable deep-learning-based phase imaging with uncertainty quantification. Optica 6, 618–629 (2019). doi: 10.1364/OPTICA.6.000618
[16] Wang, K. Q. et al. One-step robust deep learning phase unwrapping. Opt. Express 27, 15100–15115 (2019). doi: 10.1364/OE.27.015100
[17] Feng, S. J. et al. Fringe pattern analysis using deep learning. Adv. Photonics 1, 025001 (2019). doi: 10.1117/1.AP.1.2.025001
[18] Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning 775 (MIT Press, Cambridge, 2016).
[19] Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. In Proc. 18th International Conference on Medical Image Computing and Computer-Assisted Intervention 234–241 (Springer, Munich, 2015).
[20] Lempitsky, V., Vedaldi, A. & Ulyanov, D. Deep image prior. In Proc. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 9446–9454 (IEEE, Salt Lake City, 2018).
[21] Anirudh, R et al. An unsupervised approach to solving inverse problems using generative adversarial networks. Preprint at https://arxiv.org/pdf/1805.07281.pdf (2018).
[22] Liu, J. M. et al. Image restoration using total variation regularized deep image prior. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 7715–7719 (IEEE, Brighton, 2019).
[23] Jagatap, G. & Hegde, C. Phase retrieval using untrained neural network priors. In NeurIPS 2019 Workshop on Solving Inverse Problems with Deep Networks. Vancouver (2019).
[24] Shechtman, Y. et al. Phase retrieval with application to optical imaging: a contemporary overview. IEEE Signal Process. Mag. 32, 87–109 (2015). doi: 10.1109/MSP.2014.2352673
[25] Fienup, J. R. Phase retrieval algorithms: a comparison. Appl. Opt. 21, 2758–2769 (1982). doi: 10.1364/AO.21.002758
[26] Teague, M. R. Deterministic phase retrieval: a Green's function solution. J. Opt. Soc. Am. 73, 1434–1441 (1983). doi: 10.1364/JOSA.73.001434
[27] Osten, W. et al. Recent advances in digital holography [Invited]. Appl. Opt. 53, G44–G63 (2014). doi: 10.1364/AO.53.000G44
[28] Goodman, J. W. Introduction to Fourier Optics 3rd edn (Roberts and Company Publishers, Greenwoood Village, 2005).
[29] Aharon, M., Elad, M. & Bruckstein, A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 54, 4311–4322 (2006). doi: 10.1109/TSP.2006.881199
[30] Rubinstein, R., Bruckstein, A. M. & Elad, M. Dictionaries for sparse representation modeling. Proc. IEEE 98, 1045–1057 (2010). doi: 10.1109/JPROC.2010.2040551
[31] Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).
[32] Huang, G. B. et al. Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments (University of Massachusetts, 2007).
[33] Zhang, K. et al. Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26, 3142–3155 (2017). doi: 10.1109/TIP.2017.2662206
[34] Mataev, G., Elad, M. & Milanfar, P. DeepRED: deep image prior powered by RED. Preprint at https://arxiv.org/abs/1903.10176 (2019).
[35] Zhou, A. et al. Fast and robust misalignment correction of Fourier ptychographic microscopy for full field of view reconstruction. Opt. Express 26, 23661–23674 (2018). doi: 10.1364/OE.26.023661