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

414
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...
414
NMR Spectrometers: Resolution and Error Correction01:14

NMR Spectrometers: Resolution and Error Correction

920
When magnetic nuclei in a sample achieve resonance and undergo relaxation, the signal detected in NMR is an approximately exponential free induction decay. Fourier transform of an exponential decay yields a Lorentzian peak in the frequency domain. Lorentzian peaks in an NMR spectrum are defined by their amplitude, full width at half maximum, and position, where the peak width is governed by the spin-spin relaxation time alone. In real experiments, however, the applied magnetic field is rendered...
920
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.4K
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...
8.4K
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
7.7K
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

907
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
907
Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

314
The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
314

You might also read

Related Articles

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

Sort by
Same author

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

IEEE transactions on neural networks and learning systems·2026
Same author

Soluble-Salt-Template Synthesis of Nanosheets With Tunable Two-/Three-Dimensional Architectures for Electrochemical Energy Storage and Conversion.

Angewandte Chemie (International ed. in English)·2026
Same author

Commentary on "Treatment strategies for patients with ischemic mitral regurgitation: a systematic review and meta-analysis".

International journal of surgery (London, England)·2025
Same author

Optimizing s-p Orbital Overlap Between Sodium Polysulfides and Single-Atom Indium Catalyst for Efficient Sulfur Redox Reaction.

Angewandte Chemie (International ed. in English)·2024
Same author

In Situ Grown Li<sub>2</sub>Te Enhanced Lithium Metal Anode Interfacial Kinetics.

Small (Weinheim an der Bergstrasse, Germany)·2024
Same author

Routes to Bidirectional Cathodes for Reversible Aprotic Alkali Metal-CO<sub>2</sub> Batteries.

Advanced materials (Deerfield Beach, Fla.)·2024

Related Experiment Video

Updated: Nov 19, 2025

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
09:55

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

Published on: January 5, 2024

1.6K

Weighted Schatten p-Norm Low Rank Error Constraint for Image Denoising.

Jiucheng Xu1,2, Yihao Cheng1,2, Yuanyuan Ma1,2

  • 1College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China.

Entropy (Basel, Switzerland)
|January 30, 2021
PubMed
Summary

This study introduces a novel image denoising algorithm that incorporates non-local self-similarity errors. This approach enhances low-rank matrix restoration for superior image quality and robustness.

Keywords:
image denoisinglow rank error constraintlow rank representationweighted schatten p-norm

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.7K
Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
09:04

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

9.7K

Related Experiment Videos

Last Updated: Nov 19, 2025

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases
09:55

Author Spotlight: Using Hyperpolarized Xenon-129 MRI to Study Lung Diseases

Published on: January 5, 2024

1.6K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.7K
Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
09:04

Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

Published on: February 23, 2018

9.7K

Area of Science:

  • Computer Vision
  • Image Processing
  • Matrix Restoration

Background:

  • Traditional image denoising often overlooks non-local self-similarity errors.
  • Existing low-rank matrix restoration methods may not fully capture image intricacies.

Purpose of the Study:

  • To develop an advanced image denoising algorithm.
  • To integrate non-local self-similarity errors into low-rank matrix restoration.

Main Methods:

  • Introduced non-local self-similarity error into the weighted Schatten p-norm minimization model.
  • Constrained low-rank error using Schatten p-norm for improved matrix restoration.

Main Results:

  • Achieved higher peak signal-to-noise ratio (PSNR) compared to BM3D, WNNM, WSNM, and FFDNet.
  • Demonstrated superior denoising effects and visual quality on classic datasets.
  • Exhibited improved robustness and generalization capabilities.

Conclusions:

  • The proposed algorithm effectively addresses limitations of traditional denoising methods.
  • Integrating non-local self-similarity errors enhances low-rank matrix restoration for image denoising.
  • The method offers a promising advancement in image denoising performance.