A Single-Frame Superresolution Algorithm for Lab-on-a-Chip Lensless Microfluidic Imaging

Huang, Xiwei; Yu, Hao; Liu, Xu; Jiang, Yu; Yan, Mei

doi:10.1109/mdat.2015.2424418

Cited by 15 publications

(1 citation statement)

References 13 publications

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“…An alternative single-frame SR processing is thereby imperative [15]. Previous work introduces a computationally efficient single-frame SR approach by simply bilinear interpolating LR images [16].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting

Huang

Jiang

Liu

et al. 2016

Sensors

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A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated with ELMSR and CNNSR. When one captured low-resolution lensless cell image is input, an improved high-resolution cell image will be output. The experimental results show that the cell resolution is improved by 4×, and CNNSR has 9.5% improvement over the ELMSR on resolution enhancing performance. The cell counting results also match well with a commercial flow cytometer. Such ELMSR and CNNSR therefore have the potential for efficient resolution improvement in lensless blood cell counting systems towards POCT applications.

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Section: Introductionmentioning

confidence: 99%