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- Publisher Website: 10.1109/IEEECONF51394.2020.9443575
- Scopus: eid_2-s2.0-85107781866
- WOS: WOS:000681731800251
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Conference Paper: Deep Optics: Learning Cameras and Optical Computing Systems
Title | Deep Optics: Learning Cameras and Optical Computing Systems |
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Authors | |
Keywords | computational imaging computational optics optical neural networks |
Issue Date | 2020 |
Citation | Conference Record - Asilomar Conference on Signals, Systems and Computers, 2020, v. 2020-November, p. 1313-1315 How to Cite? |
Abstract | Neural networks and other advanced image processing algorithms excel in a wide variety of computer vision and imaging applications, but their high performance also comes at a high computational cost and their success is sometimes limited. Here, we review recent hybrid optical-digital strategies to computational imaging that outsource parts of the algorithm into the optical domain. Using such a co-design of optics and image processing, we can facilitate application-domain-specific cameras or compute parts of a convolutional neural network in optics. Optical computing happens at the speed of light and without any memory or power requirements, thereby opening new directions for intelligent imaging systems. |
Persistent Identifier | http://hdl.handle.net/10722/315193 |
ISSN | 2023 SCImago Journal Rankings: 0.376 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Wetzstein, Gordon | - |
dc.contributor.author | Ikoma, Hayato | - |
dc.contributor.author | Metzler, Christopher | - |
dc.contributor.author | Peng, Yifan | - |
dc.date.accessioned | 2022-08-05T10:17:59Z | - |
dc.date.available | 2022-08-05T10:17:59Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Conference Record - Asilomar Conference on Signals, Systems and Computers, 2020, v. 2020-November, p. 1313-1315 | - |
dc.identifier.issn | 1058-6393 | - |
dc.identifier.uri | http://hdl.handle.net/10722/315193 | - |
dc.description.abstract | Neural networks and other advanced image processing algorithms excel in a wide variety of computer vision and imaging applications, but their high performance also comes at a high computational cost and their success is sometimes limited. Here, we review recent hybrid optical-digital strategies to computational imaging that outsource parts of the algorithm into the optical domain. Using such a co-design of optics and image processing, we can facilitate application-domain-specific cameras or compute parts of a convolutional neural network in optics. Optical computing happens at the speed of light and without any memory or power requirements, thereby opening new directions for intelligent imaging systems. | - |
dc.language | eng | - |
dc.relation.ispartof | Conference Record - Asilomar Conference on Signals, Systems and Computers | - |
dc.subject | computational imaging | - |
dc.subject | computational optics | - |
dc.subject | optical neural networks | - |
dc.title | Deep Optics: Learning Cameras and Optical Computing Systems | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/IEEECONF51394.2020.9443575 | - |
dc.identifier.scopus | eid_2-s2.0-85107781866 | - |
dc.identifier.volume | 2020-November | - |
dc.identifier.spage | 1313 | - |
dc.identifier.epage | 1315 | - |
dc.identifier.isi | WOS:000681731800251 | - |