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

被引:14
作者
Chan, Antony C. S. [1 ,2 ]
Ho-Cheung Ng [1 ,3 ]
Bogaraju, Sharat C. V. [1 ,4 ]
So, Hayden K. H. [1 ]
Lam, Edmund Y. [1 ]
Tsia, Kevin K. [1 ]
机构
[1] Univ Hong Kong, Dept Elect & Elect Engn, Pokfulam, Hong Kong, Peoples R China
[2] CALTECH, Dept Elect Engn, Pasadena, CA 91125 USA
[3] Imperial Coll London, Dept Comp, London SW7 2AZ, England
[4] Indian Inst Space Sci & Technol, Trivandrum 695547, Kerala, India
基金
中国国家自然科学基金;
关键词
SWEPT SOURCE; MICROSCOPY; LASER;
D O I
10.1038/srep44608
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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 (approximate to 2-5GSa/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.
引用
收藏
页数:11
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