ArticleScientific reports2017
All-passive pixel super-resolution of time-stretch imaging.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Imaging flow cytometry with a real-time throughput beyond 1,000,000 events per second.Light, science & applications · 2025Article
- Super-resolution techniques for biomedical applications and challenges.Biomedical engineering letters · 2024Review
- Photonic Microfluidic Technologies for Phytoplankton Research.Biosensors · 2022Review
- Multi-ATOM: Ultrahigh-throughput single-cell quantitative phase imaging with subcellular resolution.Journal of biophotonics · 2019Article
- High-throughput imaging flow cytometry by optofluidic time-stretch microscopy.Nature protocols · 2018Article
- Improved Resolution Optical Time Stretch Imaging Based on High Efficiency In-Fiber Diffraction.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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