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Class-specific diffractive cameras based on deep learning-designed surfaces

Recently, a new diffractive camera design based on transmissive surfaces structured using deep learning is proposed. It performs class-specific imaging of target objects and all-optical deletion of other classes of objects, which will promote the development of privacy-preserving digital cameras and...

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Detalles Bibliográficos
Autores principales: Zhou, Xuxi, Wang, Shuming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9522863/
https://www.ncbi.nlm.nih.gov/pubmed/36175397
http://dx.doi.org/10.1038/s41377-022-00974-7
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author Zhou, Xuxi
Wang, Shuming
author_facet Zhou, Xuxi
Wang, Shuming
author_sort Zhou, Xuxi
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description Recently, a new diffractive camera design based on transmissive surfaces structured using deep learning is proposed. It performs class-specific imaging of target objects and all-optical deletion of other classes of objects, which will promote the development of privacy-preserving digital cameras and mission-specific data.
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spelling pubmed-95228632022-10-01 Class-specific diffractive cameras based on deep learning-designed surfaces Zhou, Xuxi Wang, Shuming Light Sci Appl News & Views Recently, a new diffractive camera design based on transmissive surfaces structured using deep learning is proposed. It performs class-specific imaging of target objects and all-optical deletion of other classes of objects, which will promote the development of privacy-preserving digital cameras and mission-specific data. Nature Publishing Group UK 2022-09-29 /pmc/articles/PMC9522863/ /pubmed/36175397 http://dx.doi.org/10.1038/s41377-022-00974-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle News & Views
Zhou, Xuxi
Wang, Shuming
Class-specific diffractive cameras based on deep learning-designed surfaces
title Class-specific diffractive cameras based on deep learning-designed surfaces
title_full Class-specific diffractive cameras based on deep learning-designed surfaces
title_fullStr Class-specific diffractive cameras based on deep learning-designed surfaces
title_full_unstemmed Class-specific diffractive cameras based on deep learning-designed surfaces
title_short Class-specific diffractive cameras based on deep learning-designed surfaces
title_sort class-specific diffractive cameras based on deep learning-designed surfaces
topic News & Views
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9522863/
https://www.ncbi.nlm.nih.gov/pubmed/36175397
http://dx.doi.org/10.1038/s41377-022-00974-7
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