The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Neural nano-optics for high-quality thin lens imaging
Open accessAbstract
Nano-optic imagers that modulate light at sub-wavelength scales could enable new applications in diverse domains ranging from robotics to medicine. Although metasurface optics offer a path to such ultra-small imagers, existing methods have achieved image quality far worse than bulky refractive alternatives, fundamentally limited by aberrations at large apertures and low f-numbers. In this work, we close this performance gap by introducing a neural nano-optics imager. We devise a fully differentiable learning framework that learns a metasurface physical structure in conjunction with a neural feature-based image reconstruction algorithm. Experimentally validating the proposed method, we achieve an order of magnitude lower reconstruction error than existing approaches. As such, we present a high-quality, nano-optic imager that combines the widest field-of-view for full-color metasurface operation while simultaneously achieving the largest demonstrated aperture of 0.5 mm at an f-number of 2.
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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Light Propagation with Phase Discontinuities: Generalized Laws of Reflection and Refraction
Yu, N., Genevet, P., Kats, M. A., Aieta, F., Tetienne, J.-P., Capasso, F., & Gaburro, Z. (2011) · Science · doi:10.1126/science.1210713 · Cited by 10,361
Flat optics with designer metasurfaces
Yu, N., & Capasso, F. (2014) · Nature Materials · doi:10.1038/nmat3839 · Cited by 5,984
Metalenses at visible wavelengths: Diffraction-limited focusing and subwavelength resolution imaging
Khorasaninejad, M., Chen, W. T., Devlin, R. C., Oh, J., Zhu, A. Y., & Capasso, F. (2016) · Science · doi:10.1126/science.aaf6644 · Cited by 3,411
Metasurface Holograms Reaching 80% Efficiency
Zheng, G., Mühlenbernd, H., Kenney, M., Li, G., Zentgraf, T., & Zhang, S. (2015) · Nature Nanotechnology · doi:10.1038/nnano.2015.2 · Cited by 2,909
Arbabi, A., Horie, Y., Bagheri, M., & Faraon, A. (2015) · Nature Nanotechnology · doi:10.1038/nnano.2015.186 · Cited by 2,813
Dielectric Gradient Metasurface Optical Elements
Lin, D., Fan, P., Hasman, E., & Brongersma, M. L. (2014) · Science · doi:10.1126/science.1253213 · Cited by 2,371
Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
Jegou, H., Douze, M., & Schmid, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_24 · Cited by 1,901
A broadband achromatic metalens for focusing and imaging in the visible
Chen, W. T., Zhu, A. Y., Sanjeev, V., Khorasaninejad, M., Shi, Z., Lee, E., & Capasso, F. (2018) · Nature Nanotechnology · doi:10.1038/s41565-017-0034-6 · Cited by 1,866
Aieta, F., Genevet, P., Kats, M. A., Yu, N., Blanchard, R., Gaburro, Z., & Capasso, F. (2012) · Nano Letters · doi:10.1021/nl302516v · Cited by 1,801
Cited by
Roadmap for Optical Metasurfaces
Kuznetsov, A. I., Brongersma, M. L., Yao, J., Chen, M. K., Levy, U., Tsai, D. P., Zheludev, N. I., Faraon, A., Arbabi, A., Yu, N., et al. (2024) · ACS Photonics · doi:10.1021/acsphotonics.3c00457 · Cited by 420
Self-citationEmpowering Metasurfaces with Inverse Design: Principles and Applications
Li, Z., Pestourie, R., Lin, Z., Johnson, S. G., & Capasso, F. (2022) · ACS Photonics · doi:10.1021/acsphotonics.1c01850 · Cited by 283
“113 The neural network performs image deconvolution using extracted image features instead of raw image intensity.”
MethodInverse design enables large-scale high-performance meta-optics reshaping virtual reality
Li, Z., Pestourie, R., Park, J.-S., Huang, Y.-W., Johnson, S. G., & Capasso, F. (2022) · Nature Communications · doi:10.1038/s41467-022-29973-3 · Cited by 280
Theory and Fundamental Limit of Quasiachromatic Metalens by Phase Delay Extension
Chen, Q., Gao, Y., Pian, S., & Ma, Y. (2023) · Physical Review Letters · doi:10.1103/physrevlett.131.193801 · Cited by 47
Metadata
- Authors
- Tseng, E., Colburn, S., Whitehead, J., Huang, L., Baek, S.-H., Majumdar, A., & Heide, F.
- Venue
- Nature Communications
- Published
- 2021-11-29
- Volume
- 12
- Issue
- 1
- Pages
- 6493-6493
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W3132875204
- Cited by
- 304
- References
- 35
- FWCI
- 11.03
- Type
- journal article
- Language
- en
- Open access
- gold
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