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All-passive pixel super-resolution of time-stretch imaging

Based on image encoding in a serial-temporal format, optical time-stretch imaging entails a stringent requirement of state-of-the-art fast data acquisition unit in order to preserve high image resolution at an ultrahigh frame rate — hampering the widespread utilities of such technology. Here, we pro...

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Detalles Bibliográficos
Autores principales: Chan, Antony C. S., Ng, Ho-Cheung, Bogaraju, Sharat C. V., So, Hayden K. H., Lam, Edmund Y., Tsia, Kevin K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5356014/
https://www.ncbi.nlm.nih.gov/pubmed/28303936
http://dx.doi.org/10.1038/srep44608
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author Chan, Antony C. S.
Ng, Ho-Cheung
Bogaraju, Sharat C. V.
So, Hayden K. H.
Lam, Edmund Y.
Tsia, Kevin K.
author_facet Chan, Antony C. S.
Ng, Ho-Cheung
Bogaraju, Sharat C. V.
So, Hayden K. H.
Lam, Edmund Y.
Tsia, Kevin K.
author_sort Chan, Antony C. S.
collection PubMed
description Based on image encoding in a serial-temporal format, optical time-stretch imaging entails a stringent requirement of state-of-the-art fast data acquisition unit in order to preserve high image resolution at an ultrahigh frame rate — hampering the widespread utilities of such technology. Here, we propose a pixel super-resolution (pixel-SR) technique tailored for time-stretch imaging that preserves pixel resolution at a relaxed sampling rate. It harnesses the subpixel shifts between image frames inherently introduced by asynchronous digital sampling of the continuous time-stretch imaging process. Precise pixel registration is thus accomplished without any active opto-mechanical subpixel-shift control or other additional hardware. Here, we present the experimental pixel-SR image reconstruction pipeline that restores high-resolution time-stretch images of microparticles and biological cells (phytoplankton) at a relaxed sampling rate (≈2–5 GSa/s)—more than four times lower than the originally required readout rate (20 GSa/s) — is thus effective for high-throughput label-free, morphology-based cellular classification down to single-cell precision. Upon integration with the high-throughput image processing technology, this pixel-SR time-stretch imaging technique represents a cost-effective and practical solution for large scale cell-based phenotypic screening in biomedical diagnosis and machine vision for quality control in manufacturing.
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spelling pubmed-53560142017-03-22 All-passive pixel super-resolution of time-stretch imaging Chan, Antony C. S. Ng, Ho-Cheung Bogaraju, Sharat C. V. So, Hayden K. H. Lam, Edmund Y. Tsia, Kevin K. Sci Rep Article Based on image encoding in a serial-temporal format, optical time-stretch imaging entails a stringent requirement of state-of-the-art fast data acquisition unit in order to preserve high image resolution at an ultrahigh frame rate — hampering the widespread utilities of such technology. Here, we propose a pixel super-resolution (pixel-SR) technique tailored for time-stretch imaging that preserves pixel resolution at a relaxed sampling rate. It harnesses the subpixel shifts between image frames inherently introduced by asynchronous digital sampling of the continuous time-stretch imaging process. Precise pixel registration is thus accomplished without any active opto-mechanical subpixel-shift control or other additional hardware. Here, we present the experimental pixel-SR image reconstruction pipeline that restores high-resolution time-stretch images of microparticles and biological cells (phytoplankton) at a relaxed sampling rate (≈2–5 GSa/s)—more than four times lower than the originally required readout rate (20 GSa/s) — is thus effective for high-throughput label-free, morphology-based cellular classification down to single-cell precision. Upon integration with the high-throughput image processing technology, this pixel-SR time-stretch imaging technique represents a cost-effective and practical solution for large scale cell-based phenotypic screening in biomedical diagnosis and machine vision for quality control in manufacturing. Nature Publishing Group 2017-03-17 /pmc/articles/PMC5356014/ /pubmed/28303936 http://dx.doi.org/10.1038/srep44608 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Chan, Antony C. S.
Ng, Ho-Cheung
Bogaraju, Sharat C. V.
So, Hayden K. H.
Lam, Edmund Y.
Tsia, Kevin K.
All-passive pixel super-resolution of time-stretch imaging
title All-passive pixel super-resolution of time-stretch imaging
title_full All-passive pixel super-resolution of time-stretch imaging
title_fullStr All-passive pixel super-resolution of time-stretch imaging
title_full_unstemmed All-passive pixel super-resolution of time-stretch imaging
title_short All-passive pixel super-resolution of time-stretch imaging
title_sort all-passive pixel super-resolution of time-stretch imaging
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5356014/
https://www.ncbi.nlm.nih.gov/pubmed/28303936
http://dx.doi.org/10.1038/srep44608
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