On the use of deep learning for computational imaging

© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. Deep learning has emerged as a class of optimization algorithms proven to be effective for a variety of inference and decision tasks. Similar algorithms, with appropriate modifications, have also been widely adopted fo...

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Bibliographic Details
Main Author: Barbastathis, George (Author)
Other Authors: Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor), Singapore-MIT Alliance in Research and Technology (SMART) (Contributor)
Format: Article
Language:English
Published: SPIE-Intl Soc Optical Eng, 2021-12-13T19:37:42Z.
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Online Access:Get fulltext
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Summary:© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only. Deep learning has emerged as a class of optimization algorithms proven to be effective for a variety of inference and decision tasks. Similar algorithms, with appropriate modifications, have also been widely adopted for computational imaging. Here, we review the basic tenets of deep learning and computational imaging, and overview recent progress in two applications: super resolution and phase retrieval.