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

Deep learning-driven super-resolution for cone-beam computed tomography: An <i>ex vivo</i> proof-of-concept study using artificially degraded micro-computed tomography data.

Imaging science in dentistry·2026

Same author

Understanding the Content and Purpose of S3 Clinical Practice Guidelines in Dentistry.

International endodontic journal·2026

Same author

Influence of Metal Artifact Reduction Algorithms on the Detection of Inferior Alveolar Canal Perforations by Zirconia Implants in CBCT Images.

Clinical and experimental dental research·2026

Same author

Diagnosis and Dentists' Treatment Preferences for Vestibular Enamel Defects-A Cross-Sectional Survey.

Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.]·2026

Same author

Advancing a Global Oral Health Research Agenda.

Journal of dental research·2026

Same author

What If the External Crown Surface of Teeth Could Predict the Pulp Chamber? A DeepSDF-Based Approach.

International endodontic journal·2026

Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026

Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026

Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026

Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026

Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026

Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 7, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Super-Resolution of Dental Panoramic Radiographs Using Deep Learning: A Pilot Study.

Hossein Mohammad-Rahimi¹, Shankeeth Vinayahalingam², Erfan Mahmoudinia³

¹Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, 10117 Berlin, Germany.

Diagnostics (Basel, Switzerland)

|March 11, 2023

Summary

This summary is machine-generated.

Super-resolution (SR) algorithms enhance low-resolution dental panoramic radiographs. The local texture estimator (LTE) deep learning model significantly outperformed other methods in improving image quality.

Keywords:

deep learning image enhancement neural networks panoramic radiographs super-resolution

More Related Videos

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

Published on: May 24, 2022

Related Experiment Videos

Last Updated: Aug 7, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

Guided Endodontics: Three-Dimensional Planning and Template-Aided Preparation of Endodontic Access Cavities

Published on: May 24, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Super-resolution (SR) algorithms can upscale low-resolution images to high-quality ones.
Improving the resolution of dental panoramic radiographs is crucial for accurate diagnosis.

Purpose of the Study:

To compare the performance of deep learning-based SR models against a conventional approach for enhancing dental panoramic radiographs.
To evaluate state-of-the-art deep learning SR models including SRCNN, SRGAN, U-Net, SwinIr, and LTE.

Main Methods:

A dataset of 888 dental panoramic radiographs was used.
Five deep learning SR models (SRCNN, SRGAN, U-Net, SwinIr, LTE) and bicubic interpolation were evaluated.
Performance metrics included Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Opinion Score (MOS) by experts.

Main Results:

The local texture estimator (LTE) model demonstrated the highest performance.
LTE achieved the best scores for MSE (7.42 ± 0.44), SSIM (0.919 ± 0.003), PSNR (39.74 ± 0.17), and MOS (3.59 ± 0.54).
All tested SR approaches showed significant MOS improvements compared to low-resolution images.

Conclusions:

Super-resolution significantly enhances the quality of dental panoramic radiographs.
The LTE deep learning model is the most effective among the evaluated methods for improving radiograph resolution.