Deep Optics: Learning Cameras and Optical Computing Systems

Gordon Wetzstein, Hayato Ikoma, Christopher Metzler, Yifan Peng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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.
Original languageEnglish (US)
Title of host publication2020 54th Asilomar Conference on Signals, Systems, and Computers
Number of pages3
ISBN (Print)9780738131269
StatePublished - Jun 3 2021
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-07-01
Acknowledgements: G.W. was supported by an NSF CAREER Award (IIS 1553333), a Sloan Fellowship, by the KAUST Office of Sponsored Research through the Visual Computing Center CCF grant, and a PECASE by the ARL. C.M. was supported by an appointment to the Intelligence Community Postdoctoral Research Fellowship Program at Stanford University administered by Oak Ridge Institute for Science and Education (ORISE) through an interagency agreement between the U.S. Department of Energy and the Office of the Director of National Intelligence (ODN).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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