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

159
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...
159

You might also read

Related Articles

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

Sort by
Same author

A Reference-Free Lens-Flare-Aware Detector for Autonomous Driving.

Sensors (Basel, Switzerland)·2026
Same author

Large Scale in vivo Acquisition, Segmentation and 3D Reconstruction of Cortical Vasculature using <math></math> Doppler Ultrasound Imaging.

Neuroinformatics·2025
Same author

Multispectral indices for real-time and non-invasive tissue ischemia monitoring using snapshot cameras.

Biomedical optics express·2024
Same author

Improving Turn Movement Count Using Cooperative Feedback.

Sensors (Basel, Switzerland)·2023
Same author

Deep Learning Tone-Mapping and Demosaicing for Automotive Vision Systems.

Sensors (Basel, Switzerland)·2023
Same author

Adaptive point cloud acquisition and upsampling for automotive lidar.

Applied optics·2023

Related Experiment Video

Updated: Jun 29, 2025

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography
07:23

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Published on: March 26, 2020

7.5K

Denoising in optical coherence tomography volumes for improved 3D visualization.

Ljubomir Jovanov, Wilfried Philips

    Optics Express
    |April 4, 2024
    PubMed
    Summary

    A new volumetric method effectively removes speckle noise from optical coherence tomography (OCT) scans. This technique enhances 3D volume quality by preserving details and improving image clarity for better medical and industrial applications.

    More Related Videos

    Thinned-skull Cortical Window Technique for In Vivo Optical Coherence Tomography Imaging
    07:28

    Thinned-skull Cortical Window Technique for In Vivo Optical Coherence Tomography Imaging

    Published on: November 19, 2012

    15.2K
    Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
    08:50

    Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

    Published on: February 9, 2019

    7.7K

    Related Experiment Videos

    Last Updated: Jun 29, 2025

    Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography
    07:23

    Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

    Published on: March 26, 2020

    7.5K
    Thinned-skull Cortical Window Technique for In Vivo Optical Coherence Tomography Imaging
    07:28

    Thinned-skull Cortical Window Technique for In Vivo Optical Coherence Tomography Imaging

    Published on: November 19, 2012

    15.2K
    Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
    08:50

    Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

    Published on: February 9, 2019

    7.7K

    Area of Science:

    • Medical Imaging
    • Optical Engineering
    • Image Processing

    Background:

    • Optical coherence tomography (OCT) is a high-resolution imaging modality crucial for medical diagnostics and industrial applications.
    • OCT provides precise information on tissue geometry and density but is limited by speckle noise, hindering the detection of small, low-intensity features.
    • Current noise reduction methods struggle to uniformly remove noise while preserving critical details in OCT volumes.

    Purpose of the Study:

    • To introduce a novel volumetric method for noise removal in OCT data.
    • To enhance the quality of rendered 3D OCT volumes by addressing speckle noise.
    • To improve the detectability of fine structures within OCT scans.

    Main Methods:

    • A new iterative volumetric algorithm was developed for noise removal in OCT data.
    • The algorithm simultaneously monitors estimated noise levels and sharpness measures.
    • Volumes are iteratively enhanced to achieve a required quality standard, ensuring uniform noise reduction and detail preservation.

    Main Results:

    • The proposed method demonstrated superior performance in noise reduction compared to reference techniques.
    • Objective quality measures confirmed the effectiveness of the algorithm.
    • Visual evaluation, including 3D auto-stereoscopic display, showed significant improvements in OCT volume visualization.

    Conclusions:

    • The developed volumetric noise removal method significantly enhances OCT image quality.
    • This technique offers a robust solution for improving detail visibility and diagnostic accuracy in OCT imaging.
    • The method holds promise for advancing applications in medicine and industry where high-fidelity OCT data is essential.