[1] |
Pittman, T. B. et al. Optical imaging by means of two-photon quantum entanglement. Phys. Rev. A 52, R3429-R3432 (1995). doi: 10.1103/PhysRevA.52.R3429 |
[2] |
Strekalov, D. V. et al. Observation of two-photon "ghost" interference and diffraction. Phys. Rev. Lett. 74, 3600-3603 (1995). doi: 10.1103/PhysRevLett.74.3600 |
[3] |
Gatti, A. et al. Ghost imaging with thermal light: comparing entanglement and classical correlation. Phys. Rev. Lett. 93, 093602 (2004). doi: 10.1103/PhysRevLett.93.093602 |
[4] |
Cheng, J. & Han, S. S. Incoherent coincidence imaging and its applicability in X-ray diffraction. Phys. Rev. Lett. 92, 093903 (2004). doi: 10.1103/PhysRevLett.92.093903 |
[5] |
Erkmen, B. I. & Shapiro, J. H. Ghost imaging: from quantum to classical to computational. Adv. Opt. Photonics 2, 405-450 (2010). doi: 10.1364/AOP.2.000405 |
[6] |
Moreau, P. A. et al. Ghost imaging using optical correlations. Laser Photonics Rev. 12, 1700143 (2018). doi: 10.1002/lpor.201700143 |
[7] |
Edgar, M. P., Gibson, G. M. & Padgett, M. J. Principles and prospects for single-pixel imaging. Nat. Photonics 13, 13-20 (2019). doi: 10.1038/s41566-018-0300-7 |
[8] |
Gibson, G. M., Johnson, S. D. & Padgett, M. J. Single-pixel imaging 12 years on: a review. Opt. Express 28, 28190-28208 (2020). doi: 10.1364/OE.403195 |
[9] |
Katz, O., Bromberg, Y. & Silberberg, Y. Compressive ghost imaging. Appl. Phys. Lett. 95, 131110 (2009). doi: 10.1063/1.3238296 |
[10] |
Zhao, C. Q. et al. Ghost imaging lidar via sparsity constraints. Appl. Phys. Lett. 101, 141123 (2012). doi: 10.1063/1.4757874 |
[11] |
Duarte, M. F. et al. Single-pixel imaging via compressive sampling. IEEE Signal Process. Mag. 25, 83-91 (2008). doi: 10.1109/MSP.2007.914730 |
[12] |
Ferri, F. et al. High-resolution ghost image and ghost diffraction experiments with thermal light. Phys. Rev. Lett. 94, 183602 (2005). doi: 10.1103/PhysRevLett.94.183602 |
[13] |
Gong, W. L. & Han, S. S. High-resolution far-field ghost imaging via sparsity constraint. Sci. Rep. 5, 9280 (2015). doi: 10.1038/srep09280 |
[14] |
Li, Z. P. et al. Super-resolution single-photon imaging at 8.2 kilometers. Opt. Express 28, 4076-4087 (2020). doi: 10.1364/OE.383456 |
[15] |
Candés, E. J., Romberg, J. K. & Tao, T. Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59, 1207-1223 (2006). doi: 10.1002/cpa.20124 |
[16] |
Donoho, D. L. Compressed sensing. IEEE Trans. Inf. Theory 52, 1289-1306 (2006). doi: 10.1109/TIT.2006.871582 |
[17] |
Eldar, Y. C. & Kutyniok, G. Compressed Sensing: Theory and Applications (New York: Cambridge University Press, 2012). |
[18] |
Brady, D. J. et al. Compressive holography. Opt. Express 17, 13040-13049 (2009). doi: 10.1364/OE.17.013040 |
[19] |
Han, S. S. et al. A review of ghost imaging via sparsity constraints. Appl. Sci. 8, 1379 (2018). doi: 10.3390/app8081379 |
[20] |
Bian, L. H. et al. Experimental comparison of single-pixel imaging algorithms. J. Optical Soc. Am. A 35, 78-87 (2018). doi: 10.1364/JOSAA.35.000078 |
[21] |
Gong, W. L. & Han, S. S. Experimental investigation of the quality of lensless super-resolution ghost imaging via sparsity constraints. Phys. Lett. A 376, 1519-1522 (2012). doi: 10.1016/j.physleta.2012.03.027 |
[22] |
Li, W. W. et al. Single-frame wide-field nanoscopy based on ghost imaging via sparsity constraints. Optica 6, 1515-1523 (2019). doi: 10.1364/OPTICA.6.001515 |
[23] |
Amitonova, L. V. & de Boer, J. F. Endo-microscopy beyond the Abbe and Nyquist limits. Light. : Sci. Appl. 9, 81 (2020). doi: 10.1038/s41377-020-0308-x |
[24] |
Sun, M. J. et al. Single-pixel three-dimensional imaging with time-based depth resolution. Nat. Commun. 7, 12010 (2016). doi: 10.1038/ncomms12010 |
[25] |
Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (Cambridge: MIT Press, 2016). |
[26] |
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436-444 (2015). doi: 10.1038/nature14539 |
[27] |
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 |
[28] |
Lyu, M. et al. Deep-learning-based ghost imaging. Sci. Rep. 7, 17865 (2017). doi: 10.1038/s41598-017-18171-7 |
[29] |
He, Y. C. et al. Ghost imaging based on deep learning. Sci. Rep. 8, 6469 (2018). doi: 10.1038/s41598-018-24731-2 |
[30] |
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 |
[31] |
Higham, C. F. et al. Deep learning for real-time single-pixel video. Sci. Rep. 8, 2369 (2018). doi: 10.1038/s41598-018-20521-y |
[32] |
Lempitsky, V., Vedaldi, A. & Ulyanov, D. Deep image prior. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (Salt Lake City, UT, USA: IEEE, 2018). |
[33] |
Dittmer, S. et al. Regularization by architecture: a deep prior approach for inverse problems. J. Math. Imaging Vis. 62, 456-470 (2020). doi: 10.1007/s10851-019-00923-x |
[34] |
Wang, F. et al. Phase imaging with an untrained neural network. Light. : Sci. Appl. 9, 77 (2020). doi: 10.1038/s41377-020-0302-3 |
[35] |
Bostan, E. et al. Deep phase decoder: self-calibrating phase microscopy with an untrained deep neural network. Optica 7, 559-562 (2020). doi: 10.1364/OPTICA.389314 |
[36] |
Van Veen, D. et al. Compressed sensing with deep image prior and learned regularization. Preprint at arXiv: 1806.06438 (2018). |
[37] |
Heckel, R. & Soltanolkotabi, M. Compressive sensing with un-trained neural networks: gradient descent finds the smoothest approximation. Proceedings of the 37th International Conference on Machine Learning (eds Ⅲ, Hal, D. and Singh, A. ). 119, 4149-4158 http://proceedings.mlr.press/v119/heckel20a/heckel20a.pdf (PMLR, 2020). |
[38] |
Zhou, K. C. & Horstmeyer, R. Diffraction tomography with a deep image prior. Opt. Express 28, 12872-12896 (2020). doi: 10.1364/OE.379200 |
[39] |
Gong, W. L. & Han, S. S. A method to improve the visibility of ghost images obtained by thermal light. Phys. Lett. A 374, 1005-1008 (2010). doi: 10.1016/j.physleta.2009.12.030 |
[40] |
Ferri, F. et al. Differential ghost imaging. Phys. Rev. Lett. 104, 253603 (2010). doi: 10.1103/PhysRevLett.104.253603 |
[41] |
Wang, C. L. et al. Airborne near infrared three-dimensional ghost imaging LiDAR via sparsity constraint. Remote Sens. 10, 732 (2018). doi: 10.3390/rs10050732 |
[42] |
Bromberg, Y., Katz, O. & Silberberg, Y. Ghost imaging with a single detector. Phys. Rev. A 79, 053840 (2009). doi: 10.1103/PhysRevA.79.053840 |
[43] |
Scully, M. O. & Zubairy, M. S. Quantum Optics (Cambridge University Press, Cambridge, 1997). |
[44] |
Deng, M. et al. On the interplay between physical and content priors in deep learning for computational imaging. Opt. Express 28, 24152-24170 (2020). doi: 10.1364/OE.395204 |
[45] |
Zhang, P. L. et al. Improving resolution by the second-order correlation of light fields. Opt. Lett. 34, 1222-1224 (2009). doi: 10.1364/OL.34.001222 |
[46] |
Wang, W. et al. Gerchberg-Saxton-like ghost imaging. Opt. Express 23, 28416-28422 (2015). doi: 10.1364/OE.23.028416 |
[47] |
Mangeat, T. et al. Super-resolved live-cell imaging using random illumination microscopy. Cell Rep. Methods 1, 100009 (2021). doi: 10.1016/j.crmeth.2021.100009 |
[48] |
Yariv, A. & Yeh, P. Photonics: Optical Electronics in Modern Communications (Oxford: Oxford University Press, 2006). |
[49] |
Healey, G. E. & Kondepudy, R. Radiometric CCD camera calibration and noise estimation. IEEE Trans. Pattern Anal. Mach. Intell. 16, 267-276 (1994). doi: 10.1109/34.276126 |
[50] |
Goodman, J. W. Statistical Optics (New York: Wiley-Blackwell, 2000). |
[51] |
Howard, A. G. et al. MobileNets: efficient convolutional neural networks for mobile vision applications. Preprint at arXiv: 1704.04861v1 (2017). |
[52] |
Glorot, X. & Bengio, Y. Understanding the difficulty of training deep feedforward neural networks. J. Mach. Learn. Res. 9, 249-256 (2010). |
[53] |
Ruder, S. An overview of gradient descent optimization algorithms. Preprint at arXiv: 1609.04747v2 (2017). |
[54] |
Ronneberger, O., Fischer, P. & Brox, T. U-Net: convolutional networks for biomedical image segmentation. Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (Munich, Germany: Springer, 2015). |