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

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

367
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...
367
Uncertainty: Overview00:59

Uncertainty: Overview

1.4K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.4K
Deconvolution01:20

Deconvolution

485
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...
485
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

456
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
456
Masking and Demasking Agents01:19

Masking and Demasking Agents

3.3K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
3.3K

You might also read

Related Articles

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

Sort by
Same author

Deployment Prior Injection for Run-Time Re-Biasable Object Detection.

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

Evaluation of UNeXt for Automatic Bone Surface Segmentation on Ultrasound Imaging in Image-Guided Pediatric Surgery.

Bioengineering (Basel, Switzerland)·2025
Same author

Evaluating the generalizability of video-based assessment of intraoperative surgical skill in capsulorhexis.

International journal of computer assisted radiology and surgery·2025
Same author

Land Resources for Wind Energy Development Requires Regionalized Characterizations.

Environmental science & technology·2024
Same author

ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer.

IEEE transactions on medical imaging·2023
Same author

Deep-learning-enabled brain hemodynamic mapping using resting-state fMRI.

NPJ digital medicine·2023
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: Dec 22, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.2K

Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks.

Rajeev Yasarla, Federico Perazzi, Vishal M Patel

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 5, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for facial image deblurring using semantic labels and an Uncertainty Guided Multi-Stream Semantic Network (UMSN). The UMSN effectively deblurs faces by processing semantic regions independently, improving results on challenging facial areas.

    Related Experiment Videos

    Last Updated: Dec 22, 2025

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    3.2K

    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Facial image deblurring is crucial for applications like recognition and enhancement.
    • Existing methods struggle with preserving fine details and handling diverse blur types.

    Purpose of the Study:

    • To develop a novel deep learning approach for high-quality facial image deblurring.
    • To leverage semantic information for more accurate and detailed deblurring results.

    Main Methods:

    • Proposing the Uncertainty Guided Multi-Stream Semantic Network (UMSN) architecture.
    • Utilizing pixel-wise semantic labels from a segmentation network.
    • Employing a confidence measure to focus on challenging facial regions (eyes, nose).
    • Training the network end-to-end for optimal performance.

    Main Results:

    • The UMSN achieves significant improvements over state-of-the-art face deblurring methods.
    • Demonstrated superior performance across three diverse face datasets.
    • Effective handling of challenging facial features due to guided training.

    Conclusions:

    • The proposed UMSN offers a robust and effective solution for facial image deblurring.
    • Exploiting semantic labels and uncertainty guidance enhances deblurring quality.
    • The method shows strong potential for real-world facial image restoration tasks.