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

Antony C. S. Chan, Ho-Cheung Ng, Sharat Chandra Varma B, Hayden Kwok-Hay So, Edmund Y Lam, Kevin K Tsia

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

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.
LanguageEnglish
Article number44608
Pages1-11
Number of pages11
JournalScientific Reports
Volume7
DOIs
Publication statusPublished - 17 Mar 2017

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pixels
sampling
cells
harnesses
phytoplankton
shift
high resolution
image resolution
computer vision
microparticles
image reconstruction
quality control
imaging techniques
data acquisition
format
image processing
readout
hardware
coding
screening

Keywords

  • Time Stretch Imaging
  • Pixel Super Resolution
  • Imaging
  • FPGA
  • High-speed optical imaging
  • Image Reconstruction pipeline
  • Imaging flow cytometry

Cite this

Chan, A. C. S., Ng, H-C., B, S. C. V., So, H. K-H., Lam, E. Y., & Tsia, K. K. (2017). All-passive pixel super-resolution of time-stretch imaging. Scientific Reports, 7, 1-11. [44608 ]. https://doi.org/10.1038/srep44608
Chan, Antony C. S. ; Ng, Ho-Cheung ; B, Sharat Chandra Varma ; So, Hayden Kwok-Hay ; Lam, Edmund Y ; Tsia, Kevin K. / All-passive pixel super-resolution of time-stretch imaging. In: Scientific Reports. 2017 ; Vol. 7. pp. 1-11.
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Chan, ACS, Ng, H-C, B, SCV, So, HK-H, Lam, EY & Tsia, KK 2017, 'All-passive pixel super-resolution of time-stretch imaging', Scientific Reports, vol. 7, 44608 , pp. 1-11. https://doi.org/10.1038/srep44608

All-passive pixel super-resolution of time-stretch imaging. / Chan, Antony C. S. ; Ng, Ho-Cheung; B, Sharat Chandra Varma; So, Hayden Kwok-Hay; Lam, Edmund Y; Tsia, Kevin K.

In: Scientific Reports, Vol. 7, 44608 , 17.03.2017, p. 1-11.

Research output: Contribution to journalArticle

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AU - B, Sharat Chandra Varma

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AU - Tsia, Kevin K

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