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

Copper-mediated amidation of alkenylzirconocenes with acyl azides: formation of enamides.

Organic letters·2013

Same author

JARID1A, JMY, and PTGER4 polymorphisms are related to ankylosing spondylitis in Chinese Han patients: a case-control study.

PloS one·2013

Same author

[The risk factors of ventilator-associated pneumonia in newborn and the changes of isolated pathogens].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition·2013

Same author

A route to phase controllable Cu2ZnSn(S(1-x)Se(x))4 nanocrystals with tunable energy bands.

Scientific reports·2013

Same author

Efficacy of an infection control program in reducing ventilator-associated pneumonia in a Chinese neonatal intensive care unit.

American journal of infection control·2013

Same author

[Effect of different forms of inorganic nitrogen on the photodegradation of antipyrine in water].

Huan jing ke xue= Huanjing kexue·2013

Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Decentralized ADMM for factorization-based Low-rank matrix estimation.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

New results on prescribed-time synchronization of complex networks via intermittent control.

Neural networks : the official journal of the International Neural Network Society·2026

Same journal

Variance-constrained multi-view ensemble broad network for imbalanced data.

Neural networks : the official journal of the International Neural Network Society·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 16, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

FSDM: An efficient video super-resolution method based on Frames-Shift Diffusion Model.

Shijie Yang¹, Chao Chen², Jie Liu²

¹State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210023, Jiangsu, China; School of Artificial Intelligence, Nanjing University, Nanjing, 210023, Jiangsu, China.

Neural Networks : the Official Journal of the International Neural Network Society

|April 5, 2025

Summary

This summary is machine-generated.

We introduce a novel Frames-Shift Diffusion Model for efficient video super-resolution (VSR). This method enhances video quality by adapting image diffusion models, offering superior performance with reduced computational cost.

Keywords:

Deep neural network Diffusion Video super-resolution

More Related Videos

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Related Experiment Videos

Last Updated: May 16, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

Published on: December 3, 2013

Area of Science:

Computer Vision
Artificial Intelligence
Image Processing

Background:

Diffusion models have advanced image super-resolution (SR).
Integrating diffusion models into video super-resolution (VSR) is computationally intensive due to complex temporal fusion modules.
Existing methods face challenges in balancing performance and computational efficiency for VSR.

Purpose of the Study:

To propose a novel and computationally efficient diffusion-based model for video super-resolution.
To adapt existing image super-resolution diffusion models for video processing without complex temporal modules.
To enhance video quality and perceptual metrics using a streamlined diffusion approach.

Main Methods:

Developed a Frames-Shift Diffusion Model by adapting image diffusion models.
Incorporated temporal information using optical flow for multi-frame fusion.
Modified the diffusion process for a smooth transition from image SR to video SR without additional parameters.

Main Results:

The Frames-Shift Diffusion Model achieves efficient frame-by-frame video processing.
The model demonstrates superior performance and enhanced perceptual quality in video super-resolution.
Achieved comparable results to state-of-the-art diffusion-based VSR methods in PSNR and SSIM metrics.

Conclusions:

The Frames-Shift Diffusion Model offers an efficient solution for video super-resolution by simplifying temporal data integration.
This approach reduces computational complexity compared to traditional VSR diffusion models.
The model effectively enhances video quality while maintaining computational efficiency.