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

High-Throughput and Integrated CRISPR/Cas12a-Based Molecular Diagnosis Using a Deep Learning Enabled Microfluidic System.

ACS nano·2024

Same author

The predictive value of intraoperative bulbocavernosus reflex monitoring for postoperative voiding function in patients with conus medullaris and cauda equina tumors: a retrospective single center study.

The spine journal : official journal of the North American Spine Society·2024

Same author

Cisplatin resistance-related transcriptome and methylome integration identifies <i>PCDHB4</i> as a novel prognostic biomarker in small cell lung cancer.

iScience·2024

Same author

Regioselective analysis of heat-induced conformational changes of β-lactoglobulin by quantitative liquid chromatography-mass spectrometry analysis of chemical labeling kinetics.

Food chemistry·2024

Same author

The complex effects of miR-146a in the pathogenesis of Alzheimer's disease.

Neural regeneration research·2024

Same author

Effects of nitrile compounds on the structure and function of soil microbial communities as revealed by metagenomes.

Environmental research·2024

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·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

See all related articles

Search research articles

Related Experiment Video

Updated: Feb 28, 2026

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

A Dual-Branch Spatial Interaction and Multi-Scale Separable Aggregation Driven Hybrid Network for Infrared Image

Jiajia Liu¹, Wenxiang Dong², Xuan Zhao²

¹Faculty Development and Teaching Evaluation Center, Civil Aviation Flight University of China, Guanghan 618307, China.

Sensors (Basel, Switzerland)

|February 27, 2026

Summary

This summary is machine-generated.

This study introduces RDSR, a novel hybrid neural network for infrared image super-resolution. RDSR effectively enhances image quality by integrating depthwise separable convolutions and self-attention, outperforming existing methods.

Keywords:

attention mechanism convolutional neural network infrared image super-resolution multi-scale feature aggregation spatial interaction

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Super-resolution Imaging of the Bacterial Division Machinery

Super-resolution Imaging of the Bacterial Division Machinery

Published on: January 21, 2013

Related Experiment Videos

Last Updated: Feb 28, 2026

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

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Super-resolution Imaging of the Bacterial Division Machinery

Super-resolution Imaging of the Bacterial Division Machinery

Published on: January 21, 2013

Area of Science:

Computer Vision
Image Processing
Deep Learning

Background:

Single Image Super-Resolution (SISR) aims to enhance image resolution.
CNNs and Transformers excel in visible image SR but face challenges with infrared images due to noise and limited long-range dependency modeling.
Infrared images present unique challenges like low signal-to-noise ratio and blurred edges.

Purpose of the Study:

To develop an effective hybrid neural network for infrared image super-resolution reconstruction.
To address the limitations of existing CNN and Transformer models in infrared imaging.
To improve detail sharpness and visual quality of infrared images.

Main Methods:

Proposed RDSR (Residual Dual-branch Separable Super-Resolution Network), a hybrid architecture.
Integrated multi-scale depthwise separable convolutions with shifted-window self-attention.
Introduced Dual-Branch Spatial Interaction (BDSI) and Multi-Scale Separable Spatial Aggregation (MSSA) modules.

Main Results:

RDSR demonstrated superior performance in terms of PSNR and SSIM for ×2 and ×4 upscaling.
Outperformed state-of-the-art CNN-based (EDSR, RCAN, RDN) and Transformer-based (SwinIR, DAT, HAT) methods.
Experimental validation on multiple public infrared image datasets confirmed effectiveness.

Conclusions:

The proposed RDSR network effectively reconstructs high-resolution infrared images.
The hybrid approach combining convolutions and self-attention is highly effective for infrared SR.
RDSR offers a promising solution for enhancing infrared image quality and detail restoration.