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: Jun 18, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

Learning to Super-Resolve Face Images via Dual-Domain Multi-scale Feature Interaction.

Licheng Liu, Jiajun Liu, Qibin Zhang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 16, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    Super-resolution Fluorescence Microscopy01:37

    Super-resolution Fluorescence Microscopy

    Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been developed.

    You might also read

    Related Articles

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

    Sort by
    Same author

    Effectiveness of heterologous mRNA vaccine boosters during an Omicron wave of COVID-19: a cross-sectional study in Macao (China).

    Journal of thoracic disease·2026
    Same author

    Fast BCIs: Leveraging Dual-Scale Time Windows with Test-Time Adaptation to Enhance Accuracy.

    IEEE transactions on bio-medical engineering·2026
    Same author

    Riemannian Acceleration for Sparse PCA With Separable Structure and Second-Order Information Exploration.

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

    Hierarchical memory-based deep reinforcement learning in simulated survival environments.

    Neural networks : the official journal of the International Neural Network Society·2026
    Same author

    Local and High-Order Consistency Coding and Adaptation for Cross-Hypergraph Node Classification.

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

    Exploring Hierarchical Cross-Modal Correlation Consistency for Partial Mismatching.

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

    CLASH-CTTA: Class-Wise Shift-Aware Hierarchical Continual Test-Time Adaptation.

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

    Voxel-based Point Cloud Geometry Compression with Space-to-Channel Context.

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

    RIGI: Rectifying Image-to-3D Generation Inconsistency via Uncertainty-Aware Learning.

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

    DA-Cal: Towards Cross-Domain Calibration in Semantic Segmentation.

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

    Multi-Dimensional Quality Assessment for Single-Image-to-3D Contents: Dataset and Model.

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

    Enhancing Underwater Light Field Images via Global Geometry-Aware Diffusion Process.

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

    This study introduces Spatial-frequency Multi-scale feature Learning Network (SMLNet) for Face Super-Resolution (FSR). SMLNet enhances facial image quality by effectively integrating spatial and frequency domain features, outperforming existing deep learning methods.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Image Processing

    Background:

    • Deep learning significantly advanced Face Super-Resolution (FSR).
    • Existing CNN and Transformer models struggle with facial structure integrity and multi-scale texture capture.
    • Limitations stem from architectural constraints and rigid receptive fields in current FSR methods.

    Purpose of the Study:

    • To propose a novel dual-domain feature interaction method for Face Super-Resolution.
    • To address limitations in facial structure preservation and multi-scale texture details.
    • To introduce the Spatial-frequency Multi-scale feature Learning Network (SMLNet).

    Main Methods:

    • Developed a dual-branch architecture for FSR.
    • Frequency branch focuses on global structures and high-frequency details.

    Related Experiment Videos

    Last Updated: Jun 18, 2026

    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
    06:25

    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

    Published on: February 12, 2014

  • Spatial branch preserves fine-grained local texture patterns.
  • Introduced a Multi-scale Spatial-frequency feature Interaction Module (MSIM) for feature aggregation.
  • MSIM integrates Multi-scale Feature Extraction Block (MFEB) and Spatial-Frequency feature Interaction Module (SFIM).

    • Main Results:

    • SMLNet demonstrated superior performance in quantitative experiments across multiple datasets.
    • Qualitative analyses confirmed the effectiveness of SMLNet in FSR.
    • Evaluations on real-world images validated the method's practical applicability.
    • The dual-domain approach successfully captured both global structures and local textures.

    Conclusions:

    • SMLNet significantly outperforms state-of-the-art FSR methods.
    • The proposed dual-domain feature interaction effectively enhances facial image quality.
    • SMLNet offers a promising solution for improving low-resolution facial images.