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: May 20, 2026

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
10:20

Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

Published on: September 5, 2019

Long&short Exposures Guided Diffusion Model for Realistic Local Motion Deblurring.

Zhongbao Yang, Lingshun Kong, Jinshan Pan

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 18, 2026
    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

    MonSter++: Unified Stereo Matching, Multi-View Stereo, and Real-Time Stereo With Monodepth Priors.

    IEEE transactions on pattern analysis and machine intelligence·2026
    Same author

    Spatial-Temporal Self-Compensating Graph Convolutional Network for Skeleton-Based Action Recognition Under Data Constraints.

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

    Multimodal detection of microplastics in human kidney stones and multi-omics exploration of renal cell metaflammation.

    Journal of hazardous materials·2026
    Same author

    HiTMM: Generative Temporal Masked Modeling of Human Interactive Motions.

    IEEE transactions on visualization and computer graphics·2026
    Same author

    MoFTSS: Motion Generation With Frequency and Text State Space Models.

    IEEE transactions on neural networks and learning systems·2026
    Same author

    Rethinking the Importance of High-Frequency Components in Transformers for Image Restoration.

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

    Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

    IEEE transactions on pattern analysis and machine intelligence·2026
    Same journal

    RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

    IEEE transactions on pattern analysis and machine intelligence·2026
    Same journal

    CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

    IEEE transactions on pattern analysis and machine intelligence·2026
    Same journal

    DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

    IEEE transactions on pattern analysis and machine intelligence·2026
    Same journal

    Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

    IEEE transactions on pattern analysis and machine intelligence·2026
    Same journal

    Learning Shape Anchors for Holistic Indoor Scene Understanding.

    IEEE transactions on pattern analysis and machine intelligence·2026
    See all related articles

    This study introduces ExpDiff, a new method for local motion deblurring using a diffusion model. It effectively removes blur from moving objects in low-light conditions, improving image quality.

    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Local motion deblurring is challenging, particularly in low signal-to-noise ratio (SNR) environments.
    • Existing methods struggle with inaccurate blur detection and unsatisfactory results when focusing only on blurred areas.

    Purpose of the Study:

    • To develop an effective diffusion model for realistic local motion deblurring.
    • To improve blur detection accuracy and image restoration quality.

    Main Methods:

    • A context-based local blur detection module leveraging contextual information for semantically coherent blur region identification.
    • A blurry-aware guided image restoration method that discriminately handles blurry and clear regions.
    • A structure-guided diffusion model utilizing long-exposure and short-exposure images for realistic restoration.

    More Related Videos

    Controlled Synthesis and Fluorescence Tracking of Highly Uniform Poly(N-isopropylacrylamide) Microgels
    11:34

    Controlled Synthesis and Fluorescence Tracking of Highly Uniform Poly(N-isopropylacrylamide) Microgels

    Published on: September 8, 2016

    Related Experiment Videos

    Last Updated: May 20, 2026

    Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
    10:20

    Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

    Published on: September 5, 2019

    Controlled Synthesis and Fluorescence Tracking of Highly Uniform Poly(N-isopropylacrylamide) Microgels
    11:34

    Controlled Synthesis and Fluorescence Tracking of Highly Uniform Poly(N-isopropylacrylamide) Microgels

    Published on: September 8, 2016

    Main Results:

    • The proposed ExpDiff method achieves realistic local motion deblurring.
    • Experimental results demonstrate favorable performance compared to state-of-the-art methods.
    • The method effectively preserves the integrity of blur regions through context-based detection.

    Conclusions:

    • ExpDiff offers a robust solution for local motion deblurring, especially in challenging low-SNR conditions.
    • The integration of context-based blur detection and a diffusion model enhances restoration realism.
    • The end-to-end trained ExpDiff method shows significant improvements over existing techniques.