Phase Extraction Neural Network (PhENN) with Coherent Modulation Imaging (CMI) for phase retrieval at low photon counts
Name
oe-28-15-21578.pdf
Description
Published version
Size
12.34 MB
Format
Unknown
Checksum (MD5)
6ac4e0b348823c4de854b201788cbd57
Author(s)
Kang, Iksung
Zhang, Fucai
Barbastathis, George
Date Issued
2020
Journal
Optics Express
Publisher
The Optical Society / Optica Publishing Group
Citation
Kang, Iksung, Zhang, Fucai and Barbastathis, George. 2020. "Phase Extraction Neural Network (PhENN) with Coherent Modulation Imaging (CMI) for phase retrieval at low photon counts." Optics Express, 28 (15).
Version
Final published version
Abstract
© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement. Imaging with low-dose light is of importance in various fields, especially when minimizing radiation-induced damage onto samples is desirable. The raw image captured at the detector plane is then predominantly a Poisson random process with Gaussian noise added due to the quantum nature of photo-electric conversion. Under such noisy conditions, highly ill-posed problems such as phase retrieval from raw intensity measurements become prone to strong artifacts in the reconstructions; a situation that deep neural networks (DNNs) have already been shown to be useful at improving. Here, we demonstrate that random phase modulation on the optical field, also known as coherent modulation imaging (CMI), in conjunction with the phase extraction neural network (PhENN) and a Gerchberg-Saxton-Fienup (GSF) approximant, further improves resilience to noise of the phase-from-intensity imaging problem. We offer design guidelines for implementing the CMI hardware with the proposed computational reconstruction scheme and quantify reconstruction improvement as function of photon count.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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/10.1364/OE.397430
