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翘曲拉伸成像的矩阵分析。

Matrix Analysis of Warped Stretch Imaging.

机构信息

Advanced Photonics Research Institute, GIST, Gwangju, 61005, Republic of Korea.

Department of Electrical Engineering, University of California, Los Angeles, California, 90095, USA.

出版信息

Sci Rep. 2017 Sep 11;7(1):11150. doi: 10.1038/s41598-017-11238-5.

Abstract

Sensitive and fast optical imaging is needed for scientific instruments, machine vision, and biomedical diagnostics. Many of the fundamental challenges are addressed with time stretch imaging, which has been used for ultrafast continuous imaging for a diverse range of applications, such as biomarker-free cell classification, the monitoring of laser ablation, and the inspection of flat panel displays. With frame rates exceeding a million scans per second, the firehose of data generated by the time stretch camera requires optical data compression. Warped stretch imaging technology utilizes nonuniform spectrotemporal optical operations to compress the image in a single-shot real-time fashion. Here, we present a matrix analysis method for the evaluation of these systems and quantify important design parameters and the spatial resolution. The key principles of the system include (1) time/warped stretch transformation and (2) the spatial dispersion of ultrashort optical pulse, which are traced with simple computation of ray-pulse matrix. Furthermore, a mathematical model is constructed for the simulation of imaging operations while considering the optical and electrical response of the system. The proposed analysis method was applied to an example time stretch imaging system via simulation and validated with experimental data.

摘要

敏感和快速的光学成像是科学仪器、机器视觉和生物医学诊断所需要的。许多基本的挑战都可以通过时间拉伸成像来解决,该技术已经被用于各种应用的超快连续成像,例如无生物标志物的细胞分类、激光烧蚀监测和平面显示器检查。时间拉伸相机生成的数据流量巨大,帧率超过每秒一百万次,因此需要光学数据压缩。扭曲拉伸成像技术利用非均匀的光谱时间光学操作,以单次实时的方式压缩图像。在这里,我们提出了一种矩阵分析方法来评估这些系统,并量化重要的设计参数和空间分辨率。系统的关键原理包括(1)时间/扭曲拉伸变换和(2)超短光脉冲的空间色散,这可以通过简单地计算光线-脉冲矩阵来追踪。此外,还构建了一个数学模型来模拟成像操作,同时考虑了系统的光学和电学响应。通过仿真对一个示例时间拉伸成像系统应用了所提出的分析方法,并通过实验数据进行了验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70eb/5593985/b7090209ef67/41598_2017_11238_Fig1_HTML.jpg

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