On the use of machine learning for solving computational imaging problems
Name
112490B.pdf
Description
Published version
Size
751.39 KB
Format
Adobe PDF
Checksum (MD5)
3784a4524b57967d8d67ba68bfe1e464
Author(s)
Barbastathis, George
Date Issued
February 2020
Journal
Proceedings of SPIE
Publisher
SPIE
Citation
Barbastathis, George. "On the use of machine learning for solving computational imaging problems." Proceedings of SPIE (February 2020) © 2020 SPIE.
Version
Final published version
Abstract
It has recently been recognized that compressed sensing, especially dictionaries and related methods, formally map to machine learning architectures, e.g. recurrent neural networks. This has led to rapid growth in algorithms and methods based on deep neural networks (but not only) for solving a variety of inverse and computational imaging problems. In this paper, we review these developments in the specific context of quantitative phase imaging and emphasizing the impact of object power spectral density and noise properties on the quality of the reconstructions.
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/10.1117/12.2554397
