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

Deconvolution01:20

Deconvolution

198
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...
198
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

1.1K
The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
1.1K
IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations

1.1K
Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
1.1K
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.1K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.1K
Upsampling01:22

Upsampling

266
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
266

You might also read

Related Articles

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

Sort by
Same author

Inferring and evaluating network medicine-based disease modules with nextflow.

Bioinformatics (Oxford, England)·2026
Same author

Generating Alzheimer's narratives using large language models.

BMC medical informatics and decision making·2026
Same author

Pushing the boundaries of robotic computed tomography: automated twin-robot CT scan with maximum reachability.

Scientific reports·2026
Same author

Imbalanced Trace Elements as Risk Factors in the Pathogenesis of Glaucoma.

Klinische Monatsblatter fur Augenheilkunde·2026
Same author

Task-Evoked Pupillary Dynamics Are Altered in Post-COVID Syndrome.

Medical sciences (Basel, Switzerland)·2026
Same author

Synthesizing vocal tract magnetic resonance imaging sequences with phoneme-aware diffusion models.

Journal of medical imaging (Bellingham, Wash.)·2026
Same journal

Serum vitamin D level and its association with vertigo frequency and severity in Meniere disease.

Scientific reports·2026
Same journal

PFA-Net: a physics-informed feature enhancement and attention network for interpretable bearing fault diagnosis under strong noise.

Scientific reports·2026
Same journal

Circulating inflammatory, redox, and apoptosis-related alterations in drug-naive idiopathic pulmonary fibrosis: an exploratory case-control study.

Scientific reports·2026
Same journal

A baseline-oriented dynamic aggregation approach for demand-side heterogeneous controllable resources.

Scientific reports·2026
Same journal

Temporal precision and accuracy in schizophrenia: an exploratory study.

Scientific reports·2026
Same journal

Prefrontal EEG spectral and nonlinear signatures of subthreshold depression during resting state and affectively valenced picture/video viewing: a participant-level analysis.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Jul 25, 2025

Live Cell Imaging of F-actin Dynamics via Fluorescent Speckle Microscopy FSM
19:16

Live Cell Imaging of F-actin Dynamics via Fluorescent Speckle Microscopy FSM

Published on: August 5, 2009

16.0K

SSN2V: unsupervised OCT denoising using speckle split.

Julia Schottenhamml1, Tobias Würfl2, Stefan B Ploner2

  • 1Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. julia.schottenhamml@fau.de.

Scientific Reports
|June 27, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new unsupervised optical coherence tomography (OCT) denoising method. The algorithm effectively reduces speckle noise while preserving crucial blood flow information for clearer retinal imaging.

More Related Videos

Author Spotlight: Advancing Neonatal Cardiac Diagnostics with Echocardiography-Derived Blood Speckle Imaging
07:13

Author Spotlight: Advancing Neonatal Cardiac Diagnostics with Echocardiography-Derived Blood Speckle Imaging

Published on: December 22, 2023

1.5K
Super-resolution Imaging of Neuronal Dense-core Vesicles
09:30

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

9.8K

Related Experiment Videos

Last Updated: Jul 25, 2025

Live Cell Imaging of F-actin Dynamics via Fluorescent Speckle Microscopy FSM
19:16

Live Cell Imaging of F-actin Dynamics via Fluorescent Speckle Microscopy FSM

Published on: August 5, 2009

16.0K
Author Spotlight: Advancing Neonatal Cardiac Diagnostics with Echocardiography-Derived Blood Speckle Imaging
07:13

Author Spotlight: Advancing Neonatal Cardiac Diagnostics with Echocardiography-Derived Blood Speckle Imaging

Published on: December 22, 2023

1.5K
Super-resolution Imaging of Neuronal Dense-core Vesicles
09:30

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

9.8K

Area of Science:

  • Biomedical Imaging
  • Medical Image Analysis
  • Optical Coherence Tomography

Background:

  • Speckle noise in optical coherence tomography (OCT) degrades image quality due to low signal-to-noise ratio.
  • Existing denoising methods often remove speckle indiscriminately, losing valuable blood flow information or causing image blurring.
  • Unsupervised denoising is crucial as noise-free OCT scans are unattainable.

Purpose of the Study:

  • To develop an unsupervised OCT denoising algorithm that reduces noise while preserving flow-related speckle information.
  • To address the limitations of current methods that either insufficiently denoise or create blurry images.
  • To improve the clinical utility of OCT by maintaining image sharpness.

Main Methods:

  • A fully unsupervised algorithm for single-frame OCT denoising (SSN2V) was developed.
  • The algorithm incorporates known operators as constraints within a neural network architecture.
  • This approach distinguishes between noise and flow information within speckle.

Main Results:

  • The proposed SSN2V method effectively reduces speckle noise in human retinal OCT B-scans.
  • The algorithm successfully preserves flow-related speckle information, maintaining image sharpness.
  • Quantitative and qualitative assessments show superior performance compared to existing methods.

Conclusions:

  • The SSN2V algorithm offers a significant advancement in unsupervised OCT denoising.
  • Preserving flow information alongside noise reduction leads to improved image quality for clinical applications.
  • This method enhances the diagnostic potential of OCT imaging by providing clearer, sharper retinal visualizations.