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Proceedings Paper

On the use of machine learning for solving computational imaging problems
Author(s): George Barbastathis
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Paper 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.

Paper Details

Date Published: 14 February 2020
PDF: 13 pages
Proc. SPIE 11249, Quantitative Phase Imaging VI, 112490B (14 February 2020); doi: 10.1117/12.2554397
Show Author Affiliations
George Barbastathis, Singapore-MIT Alliance for Research & Technology (SMART) Ctr. (Singapore)


Published in SPIE Proceedings Vol. 11249:
Quantitative Phase Imaging VI
Yang Liu; Gabriel Popescu; YongKeun Park, Editor(s)

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