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

You might also read

Related Articles

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

Sort by
Same author

PhaLP 2.0: extending the community-oriented phage lysin database with a SUBLYME pipeline for metagenomic discovery.

Database : the journal of biological databases and curation·2026
Same author

Toward explainable and generalizable data-driven modeling in real wastewater treatment plants: Utilizing bidimensional interpretable deep learning and cross-scenario transfer learning.

Journal of environmental management·2026
Same author

Enhancing zero-shot scene recognition through semantic autoencoders and visual relation transfer.

Scientific reports·2025
Same author

Multi-target prediction in volatolomics with deep neural networks: Modeling volatile organic compounds produced by Brochothrix thermosphacta under modified atmospheres.

Food research international (Ottawa, Ont.)·2025
Same author

A Comparative Survey of Vision Transformers for Feature Extraction in Texture Analysis.

Journal of imaging·2025
Same author

CAS-SFCM: Content-Aware Image Smoothing Based on Fuzzy Clustering with Spatial Information.

Journal of imaging·2025

Related Experiment Video

Updated: Dec 23, 2025

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
07:05

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Published on: February 15, 2022

2.9K

High-ISO Long-Exposure Image Denoising Based on Quantitative Blob Characterization.

Gang Wang, Carlos Lopez-Molina, Bernard De Baets

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 20, 2020
    PubMed
    Summary

    This study introduces a new computational method for blob detection and image denoising. The approach effectively measures blob features and reduces noise in images, outperforming existing methods.

    More Related Videos

    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
    07:15

    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

    Published on: July 11, 2025

    2.0K
    Counting Proteins in Single Cells with Addressable Droplet Microarrays
    12:25

    Counting Proteins in Single Cells with Addressable Droplet Microarrays

    Published on: July 6, 2018

    8.9K

    Related Experiment Videos

    Last Updated: Dec 23, 2025

    Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
    07:05

    Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

    Published on: February 15, 2022

    2.9K
    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
    07:15

    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

    Published on: July 11, 2025

    2.0K
    Counting Proteins in Single Cells with Addressable Droplet Microarrays
    12:25

    Counting Proteins in Single Cells with Addressable Droplet Microarrays

    Published on: July 6, 2018

    8.9K

    Area of Science:

    • Computer Vision
    • Image Processing
    • Computational Imaging

    Background:

    • Blob detection and image denoising are critical tasks in computer vision.
    • High-ISO long-exposure noise can exhibit blob-like structures, complicating image analysis.
    • Existing methods may struggle with noise that spatially resembles blobs.

    Purpose of the Study:

    • To develop a computational method for quantitative blob characteristic measurement.
    • To propose an effective denoising scheme for high-ISO long-exposure noise.
    • To improve blob reconstruction and reduction tasks.

    Main Methods:

    • Utilizing normalized unilateral second-order Gaussian kernels for blob measurement.
    • Implementing a blob reduction procedure as a preprocessing step for denoising.
    • Applying the proposed methods to real-world and artificially corrupted images.

    Main Results:

    • The method quantitatively measures blob position, prominence, and scale.
    • Blob reduction effectively preprocesses images for conventional denoising techniques.
    • The proposed denoising scheme demonstrates superior performance compared to state-of-the-art methods.

    Conclusions:

    • The developed method offers a robust approach to blob detection and measurement.
    • The integrated denoising scheme effectively handles complex noise patterns.
    • This work advances image processing capabilities in computer vision applications.