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

Deconvolution01:20

Deconvolution

548
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
548
Reducing Line Loss01:18

Reducing Line Loss

366
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
366
Upsampling01:22

Upsampling

588
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
588
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

442
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
442

You might also read

Related Articles

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

Sort by
Same author

Comparative metabolic safety of efavirenz-, dolutegravir-, and bictegravir-based antiretroviral regimens in HIV-infected adults: Evidence for weight-dependent lipid deterioration.

Journal of infection and public health·2026
Same author

IL-22BP Attenuates Right Ventricular Remodeling in Pulmonary Arterial Hypertension.

Clinical science (London, England : 1979)·2026
Same author

An injectable SFMA/HA-E hydrogel for sustained delivery of hucMSC-derived exosomes promotes myocardial repair after infarction.

Stem cell research & therapy·2026
Same author

Boosted Oxygen Vacancies and Lattice Oxygen Reactivity by Cobalt-Oxygen-Manganese Asymmetric Sites in Cryptomelane for Photoactivated Abatement of Volatile Organic Compounds.

ACS nano·2026
Same author

Editorial: Enhancing sports injury management through medical-engineering innovations.

Frontiers in sports and active living·2026
Same author

Multi-omics profiling identifies HMGA2 fusions as defining a distinct and prognostically favorable subtype of dedifferentiated liposarcoma with rhabdomyosarcomatous differentiation.

Human pathology·2026
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

Related Experiment Video

Updated: Jan 17, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

URFusion: Unsupervised Unified Degradation-Robust Image Fusion Network.

Han Xu, Xunpeng Yi, Chen Lu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |September 16, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces URFusion, an unsupervised network for robust image fusion that uniformly eliminates various degradations. It produces high-quality fused images by extracting content features and learning appearance representations.

    Related Experiment Videos

    Last Updated: Jan 17, 2026

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    1.0K

    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Existing image fusion methods struggle with low-quality images and specific degradations.
    • A unified approach is needed to handle diverse image degradations during fusion.

    Purpose of the Study:

    • To propose an unsupervised, unified, degradation-robust image fusion network (URFusion).
    • To uniformly eliminate various types of degradations for high-quality fused images.

    Main Methods:

    • URFusion employs three modules: intrinsic content extraction, intrinsic content fusion, and appearance representation learning and assignment.
    • It extracts degradation-free content features and uses feature-level fusion constraints.
    • Learns appearance representations and assigns high-quality statistical representations to the fused result.

    Main Results:

    • URFusion effectively eliminates degradation residues without needing ground truth.
    • Demonstrated superior fusion performance on multi-exposure and multi-modal image fusion tasks.
    • Successfully suppressed multiple types of degradations in fused images.

    Conclusions:

    • URFusion offers a robust and unified solution for image fusion across various degradations.
    • The network achieves high-quality fused images by addressing degradation challenges effectively.