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.
Original language | English |
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Article number | 44608 |
Pages (from-to) | 1-11 |
Number of pages | 11 |
Journal | Scientific Reports |
Volume | 7 |
DOIs | |
Publication status | Published (in print/issue) - 17 Mar 2017 |
Keywords
- Time Stretch Imaging
- Pixel Super Resolution
- Imaging
- FPGA
- High-speed optical imaging
- Image Reconstruction pipeline
- Imaging flow cytometry
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Sharatchandra Varma Bogaraju
- School of Engineering - Lecturer in Electronic Engineering and Embedded Systems
- Faculty Of Computing, Eng. & Built Env. - Lecturer
Person: Academic