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

MRI imaging and machine learning based radiomics for detection of mixed HCC and CCA tumors.

Annals of hepatology·2025
Same author

Converging lens radiotherapy (CLRT) employing kilovoltage x-ray source: Treatment planning study.

Medical physics·2025
Same author

Advancing Proton FLASH Radiation Therapy: Innovations, Techniques, and Clinical Potentials.

International journal of radiation oncology, biology, physics·2025
Same author

Therapy response prediction of focal cortex stimulation based on clinical parameters: a multicentre, non-interventional study protocol.

BMJ open·2025
Same author

Early-life adversity as a predictor of fibromyalgia syndrome: the central role of perceived stress over endocrine stress indicators.

Pain·2025
Same author

Perceived and endocrine acute and chronic stress indicators in fibromyalgia syndrome.

Scientific reports·2024
Same journal

Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: Apr 26, 2026

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
06:22

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

Published on: August 28, 2019

4.7K

Image Sensor Noise Parameter Estimation by Variance Stabilization and Normality Assessment.

Stanislav Pyatykh, Jurgen Hesser

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 23, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel method for estimating noise distribution parameters in image denoising. The new approach accurately estimates noise, leading to high-quality image denoising results comparable to using true noise parameters.

    More Related Videos

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.8K
    Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software
    08:22

    Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software

    Published on: July 17, 2018

    6.7K

    Related Experiment Videos

    Last Updated: Apr 26, 2026

    Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
    06:22

    Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro

    Published on: August 28, 2019

    4.7K
    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.8K
    Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software
    08:22

    Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software

    Published on: July 17, 2018

    6.7K

    Area of Science:

    • Image Processing
    • Computer Vision
    • Signal Processing

    Background:

    • Accurate image denoising necessitates understanding noise distribution's dependence on the original image.
    • These crucial noise distribution parameters are frequently unknown in practical applications.

    Purpose of the Study:

    • To develop a novel method for estimating unknown noise distribution parameters.
    • To improve the accuracy and efficiency of image denoising algorithms.

    Main Methods:

    • A variance-stabilizing transformation is employed to create an image with signal-independent noise.
    • Principal component analysis on image blocks estimates noise variance, enabling computation of original noise model parameters.
    • Image blocks are selected based on low stochastic texture strength while preserving noise distribution.

    Main Results:

    • The proposed algorithm achieves high computational efficiency and a smaller maximum estimation error than existing methods.
    • It effectively processes images with regular textures and does not require homogeneous areas.
    • Denoising performance using estimated parameters matches that achieved with true noise parameters.

    Conclusions:

    • The developed method accurately estimates noise model parameters for image denoising.
    • This technique offers a computationally efficient and robust solution for various image types.
    • It significantly enhances the quality of denoised images by accurately characterizing noise.