Meta-Learning Approach in Diffractive Lens Computational Imaging
Author:
Publisher
Pleiades Publishing Ltd
Subject
Computer Vision and Pattern Recognition
Link
https://link.springer.com/content/pdf/10.1134/S1054661822030117.pdf
Reference12 articles.
1. S. Banerji, J. Cooke, and B. Sensale-Rodriguez, “Impact of fabrication errors and refractive index on multilevel diffractive lens performance,” Sci. Rep. 10, 14608 (2020). https://doi.org/10.1038/s41598-020-71480-2
2. S. Banerji, M. Meem, A. Majumder, F. G. Vasquez, B. Sensale-Rodriguez, and R. Menon, “Imaging with flat optics: Metalenses or diffractive lenses?,” Optica 6, 805–810 (2019). https://doi.org/10.1364/OPTICA.6.000805
3. X. Dun, H. Ikoma, G. Wetzstein, Z. Wang, X. Cheng, and Y. Peng, “Learned rotationally symmetric diffractive achromat for full-spectrum computational imaging,” Optica 7, 913–922 (2020). https://doi.org/10.1364/OPTICA.394413
4. J. Engelberg and U. Levy, “The advantages of metalenses over diffractive lenses,” Nature Commun. 11, 1991 (2020). https://doi.org/10.1038/s41467-020-15972-9
5. P. Genevet, F. Capasso, F. Aieta, M. Khorasaninejad, and R. Devlin, “Recent advances in planar optics: from plasmonic to dielectric metasurfaces,” Optica 4, 139–152 (2017). https://doi.org/10.1364/OPTICA.4.000139
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