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

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

14.6K
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...
14.6K
State Space Representation01:27

State Space Representation

583
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
583
Graphical Representation of Inequalities01:28

Graphical Representation of Inequalities

220
The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
220
Control Volume and System Representations01:16

Control Volume and System Representations

1.6K
Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
1.6K
Vector Representation of Complex Numbers01:16

Vector Representation of Complex Numbers

553
Complex numbers, represented in Cartesian coordinates, can also be visualized as vectors. These vectors can be expressed in polar form, emphasizing their magnitude and angle. When a complex number is input into a function, the output is another complex number, highlighting the function's zero point from which the vector representation can originate.
Consider a function defined as the product of the complex factors in the numerator divided by the product of the complex factors in the...
553
Weighted Mean00:57

Weighted Mean

6.4K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
6.4K

You might also read

Related Articles

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

Sort by
Same author

[Breath metabolomic characteristics of schizophrenia and their potential for auxiliary diagnosis].

Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences·2026
Same author

Multimodal Image Representation Learning With Limited Visual-Tactile Data.

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

Visual dialog with semantic consistency: An external knowledge-driven approach.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Rethinking Artifact Mitigation in HDR Reconstruction: From Detection to Optimization.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2025
Same author

Global burden of lower respiratory infections attributable to cytomegalovirus, 1990-2021: a systematic analysis from the MICROBE database.

Frontiers in microbiology·2025
Same author

FGF21-engineered ADSCs promote diabetic wound healing by mitigating ferroptosis and oxidative stress via the SIRT1/NRF2/GPX4 signaling pathway.

Stem cell research & therapy·2025

Related Experiment Video

Updated: Feb 6, 2026

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons
14:02

Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

Published on: October 31, 2020

6.3K

WLDISR: Weighted Local Sparse Representation-Based Depth Image Super-Resolution for 3D Video System.

Huan Zhang, Yun Zhang, Hanli Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 24, 2018
    PubMed
    Summary

    This study introduces Weighted Local sparse representation based Depth Image Super-Resolution (WLDISR) to enhance 3D virtual view image quality. The novel approach significantly improves depth image super-resolution, leading to better virtual view synthesis.

    More Related Videos

    Super-resolution Imaging of Neuronal Dense-core Vesicles
    09:30

    Super-resolution Imaging of Neuronal Dense-core Vesicles

    Published on: July 2, 2014

    10.1K
    Super-resolution Imaging of the Bacterial Division Machinery
    08:47

    Super-resolution Imaging of the Bacterial Division Machinery

    Published on: January 21, 2013

    12.2K

    Related Experiment Videos

    Last Updated: Feb 6, 2026

    Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons
    14:02

    Super-Resolution Imaging to Study Co-Localization of Proteins and Synaptic Markers in Primary Neurons

    Published on: October 31, 2020

    6.3K
    Super-resolution Imaging of Neuronal Dense-core Vesicles
    09:30

    Super-resolution Imaging of Neuronal Dense-core Vesicles

    Published on: July 2, 2014

    10.1K
    Super-resolution Imaging of the Bacterial Division Machinery
    08:47

    Super-resolution Imaging of the Bacterial Division Machinery

    Published on: January 21, 2013

    12.2K

    Area of Science:

    • Computer Vision
    • Image Processing
    • 3D Graphics

    Background:

    • Depth images provide crucial geometric information for synthesizing virtual view images (VVIs) in 3D video systems.
    • Synthesizing high-quality VVIs requires accurate depth information, which is often limited by image resolution.

    Purpose of the Study:

    • To propose Weighted Local sparse representation based Depth Image Super-Resolution (WLDISR) schemes to enhance VVI quality.
    • To address the differing characteristics of edge and smooth regions in depth images for improved super-resolution.

    Main Methods:

    • Depth images are divided into edge and smooth patches, with separate local dictionaries learned for each.
    • A weighted sparse representation approach is employed, incorporating a weight term in the cost function to prioritize edge structures and smooth regions.
    • The WLDISR schemes (WLDISR-D, WLDISR-R, WLDISR-ALL) jointly utilize local and weighted sparse representations in dictionary learning and reconstruction.

    Main Results:

    • The proposed WLDISR schemes achieved average quality gains of 1.9-dB (WLDISR-D), 2.03-dB (WLDISR-R), and 2.16-dB (WLDISR-ALL) over state-of-the-art methods.
    • Significant improvements in the visual quality of synthesized VVIs were observed.

    Conclusions:

    • The WLDISR schemes effectively improve depth image super-resolution for 3D video systems.
    • The proposed weighted sparse representation method enhances the quality of virtual view images by better handling different depth image regions.