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 Experiment Video

Updated: Mar 30, 2026

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline
09:27

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

7.5K

Multi-Scale Patch-Based Image Restoration.

Vardan Papyan, Michael Elad

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 17, 2015
    PubMed
    Summary
    This summary is machine-generated.

    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

    Principal Uncertainty Quantification With Spatial Correlation for Image Restoration Problems.

    IEEE transactions on pattern analysis and machine intelligence·2023
    Same author

    Expert surgeons and deep learning models can predict the outcome of surgical hemorrhage from 1 min of video.

    Scientific reports·2022
    Same author

    Utility of the Simulated Outcomes Following Carotid Artery Laceration Video Data Set for Machine Learning Applications.

    JAMA network open·2022
    Same author

    Ada-LISTA: Learned Solvers Adaptive to Varying Models.

    IEEE transactions on pattern analysis and machine intelligence·2021
    Same author

    Better Compression With Deep Pre-Editing.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2021
    Same author

    Deep K-SVD Denoising.

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2021
    Same journal

    Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

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

    AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

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

    BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

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

    GoP-based Quality Enhancement on Video Compression.

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

    Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

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

    Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

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

    This study introduces a multi-scale prior for image restoration, improving upon the expected patch log likelihood (EPLL) method. The new approach enhances visual quality and quantitative performance in tasks like denoising and deblurring.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Patch-based image restoration methods are effective but suffer from artifacts due to priors applied to intermediate results.
    • The expected patch log likelihood (EPLL) method addresses this by applying priors to the final image, but can be further improved.

    Purpose of the Study:

    • To extend and enhance the EPLL method by introducing a multi-scale prior for improved image restoration.
    • To address the limitation of local priors in modeling global stochastic phenomena in patch-based algorithms.

    Main Methods:

    • Proposed a multi-scale prior approach for image restoration, applying the same prior to patches at different scales within the target image.
    • Demonstrated the method using a simple Gaussian prior and evaluated its effectiveness in image denoising, deblurring, and super-resolution.

    More Related Videos

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.8K

    Related Experiment Videos

    Last Updated: Mar 30, 2026

    Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline
    09:27

    Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

    Published on: January 30, 2019

    7.5K
    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
    13:44

    Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

    Published on: August 30, 2013

    43.8K

    Main Results:

    • The multi-scale EPLL method shows clear advantages over existing algorithms, particularly in the Gaussian case.
    • Achieved improved visual and quantitative performance in image denoising, deblurring, and super-resolution tasks.

    Conclusions:

    • The proposed multi-scale prior significantly enhances image restoration quality by better modeling global image properties.
    • This approach offers a more robust and effective solution for various image restoration challenges compared to traditional patch-based methods.