Class-specific diffractive cameras based on deep learning-designed surfaces
Abstract 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 ca...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2022-09-01
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| Series: | Light: Science & Applications |
| Online Access: | https://doi.org/10.1038/s41377-022-00974-7 |
| _version_ | 1811253719140925440 |
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| author | Xuxi Zhou Shuming Wang |
| author_facet | Xuxi Zhou Shuming Wang |
| author_sort | Xuxi Zhou |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-12T16:55:18Z |
| format | Article |
| id | doaj.art-1b48276eda2943a1b880b2ea81949fad |
| institution | Directory Open Access Journal |
| issn | 2047-7538 |
| language | English |
| last_indexed | 2024-04-12T16:55:18Z |
| publishDate | 2022-09-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Light: Science & Applications |
| spelling | doaj.art-1b48276eda2943a1b880b2ea81949fad2022-12-22T03:24:15ZengNature Publishing GroupLight: Science & Applications2047-75382022-09-011111210.1038/s41377-022-00974-7Class-specific diffractive cameras based on deep learning-designed surfacesXuxi Zhou0Shuming Wang1National Laboratory of Solid-State Microstructures, School of Physics, Nanjing UniversityNational Laboratory of Solid-State Microstructures, School of Physics, Nanjing UniversityAbstract 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.https://doi.org/10.1038/s41377-022-00974-7 |
| spellingShingle | Xuxi Zhou Shuming Wang Class-specific diffractive cameras based on deep learning-designed surfaces Light: Science & Applications |
| 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 |
| url | https://doi.org/10.1038/s41377-022-00974-7 |
| work_keys_str_mv | AT xuxizhou classspecificdiffractivecamerasbasedondeeplearningdesignedsurfaces AT shumingwang classspecificdiffractivecamerasbasedondeeplearningdesignedsurfaces |