On the use of deep learning for computational imaging
Name
114631L.pdf
Description
Published version
Size
715.13 KB
Format
Unknown
Checksum (MD5)
b0df546de97d451c293dd9c7f2ed21c8
Author(s)
Barbastathis, George
Date Issued
2020
Journal
Proceedings of SPIE - The International Society for Optical Engineering
Publisher
SPIE-Intl Soc Optical Eng
Citation
Barbastathis, George. 2020. "On the use of deep learning for computational imaging." Proceedings of SPIE - The International Society for Optical Engineering, 11463.
Version
Final published version
Abstract
© 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.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Singapore-MIT Alliance in Research and Technology (SMART)
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1117/12.2571322
