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

Video-rate gigapixel ptychography via space-time neural field representations.

Nature communications·2026

Same author

Suppression of visceral nociception by C-fiber transmission block using temporal interference sinusoidal stimulation.

Pain reports·2026

Same author

GFPT1 facilitates immune escape in lung adenocarcinoma by promoting the O-GlcNAcylation of MYC and regulating PD-L1.

Experimental cell research·2026

Same author

A pathological classification for predicting recurrence and guiding adjuvant therapy in esophageal squamous cell carcinoma following neoadjuvant immunochemotherapy: a two-center cohort study.

Frontiers in oncology·2026

Same author

Ptychography at all wavelengths.

Nature reviews. Methods primers·2026

Same author

Optimal unequal-interval phase encoding method for phase modulated holographic data storage.

Optics express·2026

Search research articles

Related Experiment Video

Updated: Dec 21, 2025

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

Published on: May 30, 2016

Image-free classification of fast-moving objects using "learned" structured illumination and single-pixel detection.

Zibang Zhang, Xiang Li, Shujun Zheng

|May 15, 2020

Summary

This study introduces a novel method for real-time object classification without image acquisition. It uses structured illumination and single-pixel detection with a convolutional neural network (CNN) for fast and accurate classification.

More Related Videos

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 9, 2013

Related Experiment Videos

Last Updated: Dec 21, 2025

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors

Published on: May 30, 2016

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 9, 2013

Area of Science:

Optics and Photonics
Computer Vision
Machine Learning

Background:

Traditional object classification relies on image acquisition, which is challenging for fast-moving objects.
Real-time classification of dynamic objects requires advanced feature extraction and analysis techniques.

Purpose of the Study:

To develop a real-time object classification method that bypasses conventional image acquisition.
To leverage structured illumination and single-pixel detection for direct feature acquisition of moving objects.

Main Methods:

Utilizing structured illumination to encode object features directly.
Employing a single-pixel detector to capture light signals modulated by object features.
Training a convolutional neural network (CNN) to interpret single-pixel measurements for classification.
Using CNN-learned features as structured patterns for illumination.

Main Results:

Demonstrated accurate and real-time classification of fast-moving objects.
Validated the effectiveness of the structured illumination and single-pixel detection approach.
Achieved classification by feeding single-pixel measurements into the trained CNN.

Conclusions:

The proposed method enables real-time object classification without image acquisition, overcoming limitations of traditional techniques.
Potential applications include high-speed cell analysis, industrial inspection, and defense systems.
The single-pixel detector approach may allow for the classification of hidden moving objects.