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

Paper-Based Colorimetric pH Test Strip Using Bio-Derived Dyes.

Biosensors·2025

Same author

Editorial on the Special Issue: "Advances in Retinal Image Processing".

Journal of imaging·2025

Same author

Biosensor for Bacterial Detection Through Color Change in Culture Medium.

Biosensors·2025

Same author

Developing and Characterization of a Biopolymeric Membrane Derived from Mature Banana Peel Biomass.

Polymers·2025

Same author

Management of an inadvertent late filtering bleb and hypotony in a scleral fixated intraocular lens implantation.

European journal of ophthalmology·2023

Same author

Preliminary Report on Optical Coherence Tomography Angiography Biomarkers in Non-Responders and Responders to Intravitreal Anti-VEGF Injection for Diabetic Macular Oedema.

Diagnostics (Basel, Switzerland)·2023

Same journal

Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.

Journal of imaging·2026

Same journal

Coronary Artery Anomalies and Anatomical Variants: Cross-Sectional Diagnostic Imaging and Clinical Background.

Journal of imaging·2026

Same journal

YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.

Journal of imaging·2026

Same journal

Radiomics-Guided Multi-Sequence Learning for Pathological Complete Response Prediction from Breast MRI with Missing Auxiliary Sequences.

Journal of imaging·2026

Same journal

Cutaneous Thermography in Arthropathies: Quantitative Imaging, Machine Learning, and Clinical Translation.

Journal of imaging·2026

Same journal

Two-Stage Dynamic Synergistic Segmentation Method for Myocardial Pathology.

Journal of imaging·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 10, 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

OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution.

Rishav Bhardwaj¹, Janarthanam Jothi Balaji², Vasudevan Lakshminarayanan¹

¹School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Journal of Imaging

|November 24, 2023

Summary

This summary is machine-generated.

Implicit neural representation can upscale images, but a new Overlapping Windows on Semi-Local Region (OW-SLR) technique improves detail by considering surrounding areas. This method enhances resolution and accuracy for medical imaging analysis.

Keywords:

OCT-A diabetic retinopathy implicit neural representation opthalmic images retina super-resolution

More Related Videos

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

Related Experiment Videos

Last Updated: Jul 10, 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

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

Area of Science:

Computer Vision
Medical Imaging
Artificial Intelligence

Background:

Implicit neural representations are advancing image upscaling to arbitrary resolutions.
Current methods using only four loci for RGB prediction lose fine details.
Semi-local region analysis offers potential for improved performance in image upscaling.

Purpose of the Study:

To introduce a novel technique, Overlapping Windows on Semi-Local Region (OW-SLR), for arbitrary image resolution upscaling.
To enhance detail preservation and prediction accuracy by incorporating semi-local image information.
To evaluate the efficacy of OW-SLR on Optical Coherence Tomography-Angiography (OCT-A) images for medical diagnostics.

Main Methods:

Developed the Overlapping Windows on Semi-Local Region (OW-SLR) technique.
OW-SLR utilizes coordinates from semi-local regions in the latent space to predict RGB values.
Applied the OW-SLR algorithm to Optical Coherence Tomography-Angiography (OCT-A) datasets, including the OCT500 dataset.

Main Results:

OW-SLR successfully upscales OCT-A images to arbitrary resolutions.
The technique demonstrates superior performance compared to existing state-of-the-art methods on the OCT500 dataset.
OW-SLR achieves improved classification of healthy and diseased retinal images (e.g., diabetic retinopathy).

Conclusions:

Incorporating semi-local information significantly improves image upscaling performance.
OW-SLR offers a robust method for enhancing resolution and detail in medical imaging.
The technique shows promise for accurate diagnostic classification of retinal conditions from OCT-A images.