CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Deep learning as phase retrieval tool for CARS spectra
Authors
Parijat Barman
Thomas Bocklitz
+4 more
Rola Houhou
Tobias Meyer
Juergen Popp
Micheal Schmitt
Publication date
1 January 2020
Publisher
Washington, DC : Soc.
Doi
Cite
Abstract
Finding efficient and reliable methods for the extraction of the phase in optical measurements is challenging and has been widely investigated. Although sophisticated optical settings, e.g. holography, measure directly the phase, the use of algorithmic methods has gained attention due to its efficiency, fast calculation and easy setup requirements. We investigated three phase retrieval methods: the maximum entropy technique (MEM), the Kramers-Kronig relation (KK), and for the first time deep learning using the Long Short-Term Memory network (LSTM). LSTM shows superior results for the phase retrieval problem of coherent anti-Stokes Raman spectra in comparison to MEM and KK. © 2020 OSA - The Optical Society. All rights reserved
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
Sustaining member
Repositorium für Naturwissenschaften und Technik
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:oa.tib.eu:123456789/7566
Last time updated on 23/07/2022