Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Predicting severe intraventricular hemorrhage in very preterm and/or very low birth weight infants: a nomogram approach.

Frontiers in pediatrics·2026

Same author

A comparison of pediatric sepsis definitions based on systemic inflammatory response syndrome and Phoenix criteria: a single-center PICU retrospective study.

Italian journal of pediatrics·2026

Same author

Knowledge, attitudes, and practices regarding hyperuricemia among physicians in internal medicine departments: a multicenter cross-sectional survey in China.

Frontiers in public health·2026

Same author

SPARC Drives Tubulointerstitial Fibrosis through Regulating the CBP-DOT1L Pathway.

International journal of biological sciences·2026

Same author

Biallelic pathogenic variants in <i>FLNB</i> are associated with paediatric steroid-resistant nephrotic syndrome via podocyte cytoskeletal dysfunction.

Journal of medical genetics·2026

Same author

A comparative study on in-situ and ex-situ microwave catalytic co-pyrolysis for phenolic bio-oil production through machine learning and response surface methodology.

Bioresource technology·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

Same journal

Three-Dimensional Modeling and Performance Analysis of Dynamic mmWave V2I Networks Based on Stochastic Geometry.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 12, 2026

Lensless On-chip Imaging of Cells Provides a New Tool for High-throughput Cell-Biology and Medical Diagnostics

Lensless On-chip Imaging of Cells Provides a New Tool for High-throughput Cell-Biology and Medical Diagnostics

Published on: December 14, 2009

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

Xiwei Huang^1,2, Yu Jiang³, Xu Liu⁴

¹Ministry of Education Key Lab of RF Circuits and Systems, Hangzhou Dianzi University, Hangzhou 310018, China. huangxiwei@hdu.edu.cn.

Sensors (Basel, Switzerland)

|November 10, 2016

Summary

This summary is machine-generated.

Super-resolution (SR) processing enhances lensless blood cell counting systems for point-of-care testing (POCT). Machine learning methods like Convolutional Neural Network based SR (CNNSR) significantly improve cell resolution and counting accuracy.

Keywords:

CMOS image sensor convolutional neural network extreme learning machine microfluidic cytometer point-of-care testing super-resolution

More Related Videos

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Related Experiment Videos

Last Updated: Mar 12, 2026

Lensless On-chip Imaging of Cells Provides a New Tool for High-throughput Cell-Biology and Medical Diagnostics

Lensless On-chip Imaging of Cells Provides a New Tool for High-throughput Cell-Biology and Medical Diagnostics

Published on: December 14, 2009

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

Lensless Fluorescent Microscopy on a Chip

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

Area of Science:

Biomedical Engineering
Optical Imaging
Machine Learning

Background:

Lensless imaging systems with microfluidics and CMOS sensors offer miniaturized point-of-care testing (POCT) for blood cell counting.
Limited resolution in lensless systems necessitates advanced processing for improved cell detection and recognition.

Purpose of the Study:

To investigate and compare machine learning-based single-frame super-resolution (SR) techniques for enhancing resolution in lensless blood cell counting.
To evaluate the effectiveness of Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR) in improving cell resolution and counting accuracy.

Main Methods:

Development and comparison of two single-frame SR algorithms: ELMSR and CNNSR.
Implementation of these algorithms on lensless blood cell counting prototypes using commercial and custom CMOS image sensors.
Quantitative evaluation of resolution enhancement and cell counting accuracy against a commercial flow cytometer.

Main Results:

A 4× improvement in cell resolution was achieved using the proposed SR methods.
CNNSR demonstrated a 9.5% higher resolution enhancement performance compared to ELMSR.
Cell counting results from the SR-enhanced lensless system showed strong agreement with a commercial flow cytometer.

Conclusions:

Both ELMSR and CNNSR effectively improve resolution in lensless blood cell counting systems.
CNNSR offers superior performance for resolution enhancement, making it highly suitable for POCT applications.
These SR techniques hold significant potential for advancing low-cost, high-performance lensless blood analysis devices.