Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.2K
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...
7.2K
Deconvolution01:20

Deconvolution

243
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
243

You might also read

Related Articles

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

Sort by
Same author

Outpatient coordination reform improves the sustainability of China's Urban Employee Basic Medical Insurance Fund.

Frontiers in public health·2024
Same author

Evaluation and correction methods for geometric errors of hydrostatic thrust bearings.

Scientific reports·2024
Same author

Predicting immunotherapy-related adverse events in late-stage non-small cell lung cancer with KARS G12C mutation treated with PD-1 inhibitors through combined assessment of LCP1 and ADPGK expression levels.

American journal of cancer research·2024
Same author

Enhancing genomic association studies in slash pine through close-range UAV-based morphological phenotyping.

Forestry research·2024
Same author

Engineering organoids-on-chips for drug testing and evaluation.

Metabolism: clinical and experimental·2024
Same author

Co-encapsulation of probiotic Lactiplantibacillus plantarum and polyphenol within novel polyvinyl alcohol/fucoidan electrospun nanofibers with improved viability and antioxidation.

International journal of biological macromolecules·2024
Same journal

An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics·2026
Same journal

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

IEEE journal of biomedical and health informatics·2026
Same journal

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026
Same journal

Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

IEEE journal of biomedical and health informatics·2026
Same journal

Cross-subject fMRI-to-Image with Visual-cortex 2D Representation and Pre-Training.

IEEE journal of biomedical and health informatics·2026
Same journal

PGCASurv: A Prior-Guided Cross-Attention Framework for Dynamic Survival Model with Longitudinal Data.

IEEE journal of biomedical and health informatics·2026
See all related articles

Related Experiment Video

Updated: Sep 3, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

488

Sparse-Based Domain Adaptation Network for OCTA Image Super-Resolution Reconstruction.

Huaying Hao, Cong Xu, Dan Zhang

    IEEE Journal of Biomedical and Health Informatics
    |July 27, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new Sparse-based domain Adaptation Super-Resolution network (SASR) to enhance low-resolution retinal Optical Coherence Tomography Angiography (OCTA) images, improving visualization of retinal vasculature for clinical analysis.

    More Related Videos

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

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

    Published on: December 15, 2023

    624
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.9K

    Related Experiment Videos

    Last Updated: Sep 3, 2025

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    488
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

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

    Published on: December 15, 2023

    624
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.9K

    Area of Science:

    • Ophthalmology
    • Medical Imaging
    • Computer Vision

    Background:

    • High-resolution Retinal Optical Coherence Tomography Angiography (OCTA) is crucial for analyzing retinal vasculature.
    • Current OCTA imaging faces a trade-off between resolution and field of view, limiting analysis of larger vascular areas.
    • Existing methods struggle to reconstruct high-resolution OCTA images from lower-resolution data effectively.

    Purpose of the Study:

    • To develop a novel Sparse-based domain Adaptation Super-Resolution network (SASR) for reconstructing high-resolution (HR) OCTA images from low-resolution (LR) inputs.
    • To improve the visualization and analysis of retinal vasculature by overcoming the resolution-field of view limitations in OCTA.
    • To validate the effectiveness of the proposed SASR method on real-world OCTA datasets and its impact on retinal structure segmentation.

    Main Methods:

    • A synthetic LR OCTA dataset was created by degrading HR images.
    • An efficient registration method was used to obtain realistic LR OCTA images.
    • A multi-level super-resolution model with a generative-adversarial strategy was employed for feature domain unification.
    • A novel sparse edge-aware loss function was designed to optimize vessel edge structures.

    Main Results:

    • The proposed SASR method demonstrated superior performance compared to state-of-the-art super-resolution techniques on two OCTA datasets.
    • The reconstructed HR OCTA images showed improved clarity and detail in retinal vasculature.
    • Performance evaluation on retina structure segmentation tasks confirmed the effectiveness of the SASR approach.

    Conclusions:

    • The SASR network effectively reconstructs high-resolution OCTA images from low-resolution inputs, enhancing clinical analysis of retinal vasculature.
    • The method successfully unifies synthetic and realistic LR images in the feature domain, improving reconstruction quality.
    • SASR offers a promising solution for overcoming resolution limitations in OCTA imaging, with validated benefits for downstream applications like structure segmentation.