Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application

Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application

Abstract A deep learning algorithm for single-shot phase retrieval under a conventional microscope is proposed and investigated. The algorithm has been developed using the context aggregation network architecture; it requires a single input grayscale image to predict an output phase profile through...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Kitsada Thadson, Sarinporn Visitsattapongse, Suejit Pechprasarn
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/66a0ed44286f4d069816c08d8aea7f4c
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:66a0ed44286f4d069816c08d8aea7f4c
record_format dspace
spelling oai:doaj.org-article:66a0ed44286f4d069816c08d8aea7f4c2021-12-02T18:50:47ZDeep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application10.1038/s41598-021-95593-42045-2322https://doaj.org/article/66a0ed44286f4d069816c08d8aea7f4c2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95593-4https://doaj.org/toc/2045-2322Abstract A deep learning algorithm for single-shot phase retrieval under a conventional microscope is proposed and investigated. The algorithm has been developed using the context aggregation network architecture; it requires a single input grayscale image to predict an output phase profile through deep learning-based pattern recognition. Surface plasmon resonance imaging has been employed as an example to demonstrate the capability of the deep learning-based method. The phase profiles of the surface plasmon resonance phenomena have been very well established and cover ranges of phase transitions from 0 to 2π rad. We demonstrate that deep learning can be developed and trained using simulated data. Experimental validation and a theoretical framework to characterize and quantify the performance of the deep learning-based phase retrieval method are reported. The proposed deep learning-based phase retrieval performance was verified through the shot noise model and Monte Carlo simulations. Refractive index sensing performance comparing the proposed deep learning algorithm and conventional surface plasmon resonance measurements are also discussed. Although the proposed phase retrieval-based algorithm cannot achieve a typical detection limit of 10–7 to 10–8 RIU for phase measurement in surface plasmon interferometer, the proposed artificial-intelligence-based approach can provide at least three times lower detection limit of 4.67 × 10–6 RIU compared to conventional intensity measurement methods of 1.73 × 10–5 RIU for the optical energy of 2500 pJ with no need for sophisticated optical interferometer instrumentation.Kitsada ThadsonSarinporn VisitsattapongseSuejit PechprasarnNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Kitsada Thadson
Sarinporn Visitsattapongse
Suejit Pechprasarn
Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application
description Abstract A deep learning algorithm for single-shot phase retrieval under a conventional microscope is proposed and investigated. The algorithm has been developed using the context aggregation network architecture; it requires a single input grayscale image to predict an output phase profile through deep learning-based pattern recognition. Surface plasmon resonance imaging has been employed as an example to demonstrate the capability of the deep learning-based method. The phase profiles of the surface plasmon resonance phenomena have been very well established and cover ranges of phase transitions from 0 to 2π rad. We demonstrate that deep learning can be developed and trained using simulated data. Experimental validation and a theoretical framework to characterize and quantify the performance of the deep learning-based phase retrieval method are reported. The proposed deep learning-based phase retrieval performance was verified through the shot noise model and Monte Carlo simulations. Refractive index sensing performance comparing the proposed deep learning algorithm and conventional surface plasmon resonance measurements are also discussed. Although the proposed phase retrieval-based algorithm cannot achieve a typical detection limit of 10–7 to 10–8 RIU for phase measurement in surface plasmon interferometer, the proposed artificial-intelligence-based approach can provide at least three times lower detection limit of 4.67 × 10–6 RIU compared to conventional intensity measurement methods of 1.73 × 10–5 RIU for the optical energy of 2500 pJ with no need for sophisticated optical interferometer instrumentation.
format article
author Kitsada Thadson
Sarinporn Visitsattapongse
Suejit Pechprasarn
author_facet Kitsada Thadson
Sarinporn Visitsattapongse
Suejit Pechprasarn
author_sort Kitsada Thadson
title Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application
title_short Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application
title_full Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application
title_fullStr Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application
title_full_unstemmed Deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application
title_sort deep learning-based single-shot phase retrieval algorithm for surface plasmon resonance microscope based refractive index sensing application
publisher Nature Portfolio
publishDate 2021
url https://doaj.org/article/66a0ed44286f4d069816c08d8aea7f4c
work_keys_str_mv AT kitsadathadson deeplearningbasedsingleshotphaseretrievalalgorithmforsurfaceplasmonresonancemicroscopebasedrefractiveindexsensingapplication
AT sarinpornvisitsattapongse deeplearningbasedsingleshotphaseretrievalalgorithmforsurfaceplasmonresonancemicroscopebasedrefractiveindexsensingapplication
AT suejitpechprasarn deeplearningbasedsingleshotphaseretrievalalgorithmforsurfaceplasmonresonancemicroscopebasedrefractiveindexsensingapplication
_version_ 1718377508148084736

Ejemplares similares