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

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

9.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
9.8K
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

829
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
829
Weighted Mean00:57

Weighted Mean

7.3K
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...
7.3K
Structural Classification of Joints01:20

Structural Classification of Joints

8.6K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
8.6K
Deconvolution01:20

Deconvolution

676
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...
676
Reducing Line Loss01:18

Reducing Line Loss

442
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...
442

You might also read

Related Articles

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

Sort by
Same author

In-Situ Constructed Cations for 2D/3D Perovskite Heterostructure for Stable and Efficient Photovoltaics.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Variance-constrained multi-view ensemble broad network for imbalanced data.

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

Isotopic constraints in methane inversions reveal larger trends in wetland emissions with improved linkage to terrestrial water storage.

Nature communications·2026
Same author

Clone and characterization of a cytochrome P450 gene for drought tolerance in rice.

BMC plant biology·2026
Same author

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

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

Related Experiment Video

Updated: Mar 24, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

264

Weighted Joint Sparse Representation for Removing Mixed Noise in Image.

Licheng Liu, Long Chen, C L Philip Chen

    IEEE Transactions on Cybernetics
    |March 10, 2016
    PubMed
    Summary

    This study introduces a weighted joint sparse representation (WJSR) model to effectively remove mixed noise and outliers from images. The proposed WJSR method demonstrates superior denoising performance compared to existing techniques.

    Related Experiment Videos

    Last Updated: Mar 24, 2026

    Automated Joint Space Detection Improves Bone Segmentation Accuracy
    06:45

    Automated Joint Space Detection Improves Bone Segmentation Accuracy

    Published on: November 28, 2025

    264

    Area of Science:

    • Computer Vision
    • Image Processing
    • Signal Processing

    Background:

    • Joint sparse representation (JSR) is a powerful technique for image processing.
    • Conventional JSR models are susceptible to noise and outliers, limiting their robustness.

    Purpose of the Study:

    • To develop a robust weighted joint sparse representation (WJSR) model.
    • To address the limitations of conventional JSR in handling noisy and outlier-corrupted data.

    Main Methods:

    • Proposing a WJSR model to exploit shared information while mitigating outlier influence.
    • Introducing a weighted simultaneous orthogonal matching pursuit algorithm for efficient model solving.
    • Applying WJSR for mixed noise removal by jointly coding nonlocal similar image patches.

    Main Results:

    • The WJSR model effectively reduces the impact of outliers and noise.
    • The proposed denoising method shows improved performance by integrating global priors and sparse errors.
    • Experimental results confirm the superiority of the WJSR-based denoising approach.

    Conclusions:

    • The WJSR model offers a robust solution for image denoising in the presence of mixed noise and outliers.
    • The developed algorithm provides an efficient way to approximate the optimal solution for WJSR.
    • The proposed method outperforms current state-of-the-art techniques for mixed noise removal.