Eckart, A. & Genzel, R. Stellar proper motions in the central 0.1 pc of the Galaxy. Monthly Notices of the Royal Astronomical Society 284, 576–598 (1997).
M. Solan et al., Towards a greater understanding of pattern, scale and process in marine benthic systems: a picture is worth a thousand worms. J. Exp. Mar. Biol. Ecol. 285–286, 313–338 (2003)
Google Scholar
Tan, R. T. Visibility in bad weather from a single image. in 2008 IEEE Conference on Computer Vision and Pattern Recognition 1–8 (2008). https://doi.org/10.1109/CVPR.2008.4587643.
V. Ntziachristos, Going deeper than microscopy: the optical imaging frontier in biology. Nat. Methods 7, 603–614 (2010)
Google Scholar
N. Ji, D.E. Milkie, E. Betzig, Adaptive optics via pupil segmentation for high-resolution imaging in biological tissues. Nat. Methods 7, 141–147 (2010)
Google Scholar
K. He, J. Sun, X. Tang, Single Image Haze Removal Using Dark Channel Prior. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2341–2353 (2011)
Google Scholar
J. Bertolotti et al., Non-invasive imaging through opaque scattering layers. Nature 491, 232–234 (2012)
ADS
Google Scholar
A.P. Mosk, A. Lagendijk, G. Lerosey, M. Fink, Controlling waves in space and time for imaging and focusing in complex media. Nat. Photonics 6, 283–292 (2012)
ADS
Google Scholar
O. Katz, P. Heidmann, M. Fink, S. Gigan, Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations. Nature Photon 8, 784–790 (2014)
ADS
Google Scholar
S.-C. Huang, B.-H. Chen, Y.-J. Cheng, An Efficient Visibility Enhancement Algorithm for Road Scenes Captured by Intelligent Transportation Systems. IEEE Trans. Intell. Transp. Syst. 15, 2321–2332 (2014)
Google Scholar
S. Li, M. Deng, J. Lee, A. Sinha, G. Barbastathis, Imaging through glass diffusers using densely connected convolutional networks. Optica, OPTICA 5, 803–813 (2018)
ADS
Google Scholar
Y. Li, Y. Xue, L. Tian, Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media. Optica 5, 1181 (2018)
ADS
Google Scholar
D.B. Lindell, G. Wetzstein, Three-dimensional imaging through scattering media based on confocal diffuse tomography. Nat Commun 11, 4517 (2020)
ADS
Google Scholar
J.W. Goodman, W.H. Huntley, D.W. Jackson, M. Lehmann, Wavefront-reconstruction imaging through random media. Appl. Phys. Lett. 8, 311–313 (1966)
ADS
Google Scholar
H. Kogelnik, K.S. Pennington, Holographic Imaging Through a Random Medium. J. Opt. Soc. Am. 58, 273 (1968)
Google Scholar
S. Popoff, G. Lerosey, M. Fink, A.C. Boccara, S. Gigan, Image transmission through an opaque material. Nat Commun 1, 81 (2010)
ADS
Google Scholar
J. Li et al., Conjugate adaptive optics in widefield microscopy with an extended-source wavefront sensor. Optica 2, 682 (2015)
ADS
Google Scholar
E. Edrei, G. Scarcelli, Optical imaging through dynamic turbid media using the Fourier-domain shower-curtain effect. Optica 3, 71 (2016)
ADS
Google Scholar
X. Li, J.A. Greenberg, M.E. Gehm, Single-shot multispectral imaging through a thin scatterer. Optica, OPTICA 6, 864–871 (2019)
ADS
Google Scholar
M. Jang et al., Relation between speckle decorrelation and optical phase conjugation (OPC)-based turbidity suppression through dynamic scattering media: a study on in vivo mouse skin. Biomed. Opt. Express 6, 72 (2015)
Google Scholar
S.G. Narasimhan, S.K. Nayar, Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25, 713–724 (2003)
Google Scholar
E.A. Bucher, Computer Simulation of Light Pulse Propagation for Communication Through Thick Clouds. Appl. Opt. 12, 2391 (1973)
ADS
Google Scholar
A. Lopez, E. Nezry, R. Touzi, H. Laur, Structure detection and statistical adaptive speckle filtering in SAR images. Int. J. Remote Sens. 14, 1735–1758 (1993)
Google Scholar
Lohmann, A. W., Weigelt, G. & Wirnitzer, B. Speckle masking in astronomy: triple correlation theory and applications. Appl. Opt., AO 22, 4028–4037 (1983).
Roggemann, M. C., Welsh, B. M. & Hunt, B. R. Imaging Through Turbulence. (CRC Press, 1996).
J.S. Jaffe, K.D. Moore, J. Mclean, M.R. Strand, Underwater optical imaging: Status and prospects. Oceanography 14, 64–66 (2001)
Google Scholar
Schettini, R. & Corchs, S. Underwater Image Processing: State of the Art of Restoration and Image Enhancement Methods. EURASIP Journal on Advances in Signal Processing 2010, (2010).
Z. Jia et al., A two-step approach to see-through bad weather for surveillance video quality enhancement. Mach. Vis. Appl. 23, 1059–1082 (2012)
Google Scholar
Tarel, J.-P. & Hautière, N. Fast visibility restoration from a single color or gray level image. in 2009 IEEE 12th International Conference on Computer Vision 2201–2208 (2009). https://doi.org/10.1109/ICCV.2009.5459251.
M. Johnson-Roberson et al., High-Resolution Underwater Robotic Vision-Based Mapping and Three-Dimensional Reconstruction for Archaeology. Journal of Field Robotics 34, 625–643 (2017)
Google Scholar
Hao, Z., You, S., Li, Y., Li, K. & Lu, F. Learning From Synthetic Photorealistic Raindrop for Single Image Raindrop Removal. in Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops 0–0 (2019).
Majer, F., Yan, Z., Broughton, G., Ruichek, Y. & Krajník, T. Learning to see through haze: Radar-based Human Detection for Adverse Weather Conditions. in 2019 European Conference on Mobile Robots (ECMR) 1–7 (2019). doi:https://doi.org/10.1109/ECMR.2019.8870954.
Popoff, S. M. et al. Measuring the Transmission Matrix in Optics: An Approach to the Study and Control of Light Propagation in Disordered Media. Physical Review Letters 104, (2010).
Goodman, J. W. Speckle Phenomena in Optics: Theory and Applications. (Roberts and Company Publishers, 2007).
D.B. Conkey, A.M. Caravaca-Aguirre, R. Piestun, High-speed scattering medium characterization with application to focusing light through turbid media. Opt. Express 20, 1733 (2012)
ADS
Google Scholar
Wang, K. et al. Direct wavefront sensing for high-resolution in vivo imaging in scattering tissue. Nature Communications 6, (2015).
I.M. Vellekoop, A.P. Mosk, Focusing coherent light through opaque strongly scattering media. Opt. Lett. 32, 2309 (2007)
ADS
Google Scholar
I.M. Vellekoop, A. Lagendijk, A.P. Mosk, Exploiting disorder for perfect focusing. Nat. Photonics 4, 320–322 (2010)
Google Scholar
R. Horstmeyer, H. Ruan, C. Yang, Guidestar-assisted wavefront-shaping methods for focusing light into biological tissue. Nat. Photonics 9, 563–571 (2015)
ADS
Google Scholar
M. Nixon et al., Real-time wavefront shaping through scattering media by all-optical feedback. Nat. Photonics 7, 919–924 (2013)
ADS
Google Scholar
O. Katz, E. Small, Y. Silberberg, Looking around corners and through thin turbid layers in real time with scattered incoherent light. Nat. Photonics 6, 549–553 (2012)
ADS
Google Scholar
S. Feng, C. Kane, P.A. Lee, A.D. Stone, Correlations and Fluctuations of Coherent Wave Transmission through Disordered Media. Phys. Rev. Lett. 61, 834–837 (1988)
ADS
Google Scholar
I. Freund, M. Rosenbluh, S. Feng, Memory Effects in Propagation of Optical Waves through Disordered Media. Phys. Rev. Lett. 61, 2328–2331 (1988)
ADS
Google Scholar
Edrei, E. & Scarcelli, G. Memory-effect based deconvolution microscopy for super-resolution imaging through scattering media. Scientific Reports 6, (2016).
W. Yang, G. Li, G. Situ, Imaging through scattering media with the auxiliary of a known reference object. Sci. Rep. 8, 9614 (2018)
ADS
Google Scholar
He, H., Guan, Y. & Zhou, J. Image restoration through thin turbid layers by correlation with a known object. Opt. Express, OE 21, 12539–12545 (2013).
X. Wang et al., Prior-information-free single-shot scattering imaging beyond the memory effect. Opt. Lett. 44, 1423 (2019)
ADS
Google Scholar
Yang, M. et al. Deep hybrid scattering image learning. J. Phys. D: Appl. Phys. 52, 115105 (2019).
Lyu, M., Wang, H., Li, G., Zheng, S. & Situ, G. Learning-based lensless imaging through optically thick scattering media. AP 1, 036002 (2019).
Y. Rivenson et al., Deep learning microscopy. Optica, OPTICA 4, 1437–1443 (2017)
ADS
Google Scholar
Y. Rivenson et al., Deep Learning Enhanced Mobile-Phone Microscopy. ACS Photonics (2018). https://doi.org/10.1021/acsphotonics.8b00146
Article
Google Scholar
E. Nehme, L.E. Weiss, T. Michaeli, Y. Shechtman, Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica, OPTICA 5, 458–464 (2018)
ADS
Google Scholar
H. Wang et al., Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods 16, 103–110 (2019)
Google Scholar
Y. Rivenson et al., Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat Biomed Eng 3, 466–477 (2019)
Google Scholar
Y. Wu et al., Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning. Nat. Methods 16, 1323–1331 (2019)
Google Scholar
Y. Wu et al., Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery. Optica 5, 704 (2018)
ADS
Google Scholar
Wu, Y. et al. Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram. Light: Science & Applications 8, 1–7 (2019).
T. Liu et al., Deep learning-based super-resolution in coherent imaging systems. Sci. Rep. 9, 1–13 (2019)
ADS
Google Scholar
Liu, T. et al. Deep learning-based color holographic microscopy. Journal of Biophotonics 12, e201900107 (2019).
G. Barbastathis, A. Ozcan, G. Situ, On the use of deep learning for computational imaging. Optica, OPTICA 6, 921–943 (2019)
ADS
Google Scholar
Wang, F. et al. Phase imaging with an untrained neural network. Light: Science & Applications 9, 77 (2020).
Malkiel, I. et al. Plasmonic nanostructure design and characterization via Deep Learning. Light: Science & Applications 7, (2018).
D. Liu, Y. Tan, E. Khoram, Z. Yu, Training Deep Neural Networks for the Inverse Design of Nanophotonic Structures. ACS Photonics 5, 1365–1369 (2018)
Google Scholar
Peurifoy, J. et al. Nanophotonic particle simulation and inverse design using artificial neural networks. Science Advances 4, eaar4206 (2018).
W. Ma, F. Cheng, Y. Liu, Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials. ACS Nano 12, 6326–6334 (2018)
Google Scholar
Luo, Y. et al. Design of task-specific optical systems using broadband diffractive neural networks. Light: Science & Applications 8, 1–14 (2019).
M. Veli et al., Terahertz pulse shaping using diffractive surfaces. Nat. Commun. 12, 37 (2021)
ADS
Google Scholar
D. Psaltis, D. Brady, X.G. Gu, S. Lin, Holography in artificial neural networks. Nature 343, 325–330 (1990)
ADS
Google Scholar
Y. Shen et al., Deep learning with coherent nanophotonic circuits. Nat. Photonics 11, 441–446 (2017)
ADS
Google Scholar
Chang, J., Sitzmann, V., Dun, X., Heidrich, W. & Wetzstein, G. Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification. Scientific Reports 8, (2018).
X. Lin et al., All-optical machine learning using diffractive deep neural networks. Science 361, 1004 (2018)
ADS
MathSciNet
MATH
Google Scholar
N.M. Estakhri, B. Edwards, N. Engheta, Inverse-designed metastructures that solve equations. Science 363, 1333–1338 (2019)
ADS
MathSciNet
MATH
Google Scholar
J. Li, D. Mengu, Y. Luo, Y. Rivenson, A. Ozcan, Class-specific differential detection in diffractive optical neural networks improves inference accuracy. Adv. Photon. 1, 1 (2019)
Google Scholar
D. Mengu, Y. Luo, Y. Rivenson, A. Ozcan, Analysis of Diffractive Optical Neural Networks and Their Integration With Electronic Neural Networks. IEEE J. Sel. Top. Quantum Electron. 26, 1–14 (2020)
Google Scholar
Mengu, D. et al. Misalignment resilient diffractive optical networks. Nanophotonics 0, (2020).
Li, J. et al. Spectrally encoded single-pixel machine vision using diffractive networks. Science Advances 7, eabd7690 (2021).
O. Kulce, D. Mengu, Y. Rivenson, A. Ozcan, All-optical information-processing capacity of diffractive surfaces. Light Sci Appl 10, 25 (2021)
Google Scholar
B. Rahmani, D. Loterie, G. Konstantinou, D. Psaltis, C. Moser, Multimode optical fiber transmission with a deep learning network. Light Sci Appl 7, 1–11 (2018)
Google Scholar
Bai, B. et al. Pathological crystal imaging with single-shot computational polarized light microscopy. Journal of Biophotonics 13, e201960036 (2020).
T. Liu et al., Deep Learning-Based Holographic Polarization Microscopy. ACS Photonics 7, 3023–3034 (2020)
Google Scholar
LeCun, Y. et al. Handwritten Digit Recognition with a Back-Propagation Network. in Advances in Neural Information Processing Systems 2 (ed. Touretzky, D. S.) 396–404 (Morgan-Kaufmann, 1990).
Benesty, J., Chen, J., Huang, Y. & Cohen, I. Pearson Correlation Coefficient. in Noise Reduction in Speech Processing vol. 2 1–4 (Springer Berlin Heidelberg, 2009).
Wu, T., Dong, J., Shao, X. & Gigan, S. Imaging through a thin scattering layer and jointly retrieving the point-spread-function using phase-diversity. Opt. Express, OE 25, 27182–27194 (2017).
X. Xu et al., Imaging of objects through a thin scattering layer using a spectrally and spatially separated reference. Opt. Express 26, 15073 (2018)
ADS
Google Scholar
Hofer, M., Soeller, C., Brasselet, S. & Bertolotti, J. Wide field fluorescence epi-microscopy behind a scattering medium enabled by speckle correlations. Opt. Express, OE 26, 9866–9881 (2018).
S. Lowenthal, D. Joyeux, Speckle Removal by a Slowly Moving Diffuser Associated with a Motionless Diffuser. J. Opt. Soc. Am. 61, 847 (1971)
ADS
Google Scholar
Kingma, D. P. & Ba, J. Adam: A Method for Stochastic Optimization. arXiv:1412.6980. [cs] (2014).
Rahman, M. S. S., Li, J., Mengu, D., Rivenson, Y. & Ozcan, A. Ensemble learning of diffractive optical networks. Light: Science & Applications 10, 14 (2021).