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 developed.

You might also read

Related Articles

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

Sort by

Same author

Author Correction: Phenome-wide analysis of copy number variants in 470,727 UK Biobank genomes.

Nature·2026

Same author

Phenome-wide analysis of copy number variants in 470,727 UK Biobank genomes.

Nature·2026

Same author

Rare heterozygous missense variants in VSX2 are associated with retinal detachment.

PLoS genetics·2026

Same author

Population-scale sequencing resolves determinants of persistent EBV DNA.

Nature·2026

Same author

Differentiated characteristics, sustainability performance and preferences among small-scale aquaculture producers: implications for sustainable intensification.

Sustainability science·2026

Same author

Population-scale sequencing resolves correlates and determinants of latent Epstein-Barr Virus infection.

bioRxiv : the preprint server for biology·2025

Search research articles

Related Experiment Video

Updated: May 17, 2026

Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

Published on: January 3, 2017

Model-controlled flooding with applications to image reconstruction and segmentation.

Quanli Wang¹, Mike West

¹Duke University, Department of Statistical Science, Durham, North Carolina 27708-0251.

Journal of Electronic Imaging

|October 11, 2012

Summary

Model-controlled flooding (MCF) enhances image reconstruction and segmentation by integrating prior information into simulations. This novel approach improves accuracy in various applications like object detection and biological imaging.

More Related Videos

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Related Experiment Videos

Last Updated: May 17, 2026

Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

Published on: January 3, 2017

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Published on: August 13, 2014

Area of Science:

Image analysis
Computer vision
Scientific imaging

Background:

Traditional image segmentation methods like watershed transform have limitations in incorporating prior knowledge.
Existing image reconstruction techniques offer limited flexibility in modifying image components.

Purpose of the Study:

To introduce a novel framework, model-controlled flooding (MCF), for improved image reconstruction and segmentation.
To extend the capabilities of watershed transforms and connected attribute filters by integrating a priori information.
To demonstrate the adaptability and effectiveness of MCF across diverse imaging applications.

Main Methods:

MCF integrates user-defined or default model functions into watershed flooding simulations for seeding, region growing, and stopping rules.
The framework modifies connected components of grayscale images, extending connected attribute filters.
A size transform, extending grayscale area opening and attribute thickening/thinning, is used for demonstration.

Main Results:

MCF enables customized simulations by incorporating prior information about image objects.
The method provides enhanced flexibility in image reconstruction, defining images with desirable features for segmentation.
MCF achieved benchmark error rates significantly lower than existing methods in concealed object detection, biological speckle counting, and microscopic image analysis.

Conclusions:

MCF offers a flexible and powerful framework for advanced image reconstruction and segmentation.
The integration of prior information significantly improves segmentation accuracy and image quality.
MCF demonstrates broad applicability and adaptability to new imaging contexts, outperforming current state-of-the-art algorithms.