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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

376
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
376
Weighted Mean00:57

Weighted Mean

7.2K
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...
7.2K
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

1.3K
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...
1.3K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

9.7K
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...
9.7K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

9.0K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
9.0K
What are Estimates?01:06

What are Estimates?

9.0K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
9.0K

You might also read

Related Articles

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

Sort by
Same author

Tailorable porous collagen hydrogels as a physiologically relevant platform for extrachromosomal DNA-associated colorectal cancer research.

Theranostics·2026
Same author

Sustainable removal of carcinogenic heavy metal in wastewater using activated carbon derived from coconut biomass.

Environmental geochemistry and health·2026
Same author

Transabdominal ultrasound sliding sign for predicting intra-abdominal adhesions in repeat cesarean delivery: a prospective observational study from Vietnam.

AJOG global reports·2026
Same author

Correlation between demographic factors and Autism Spectrum Disorder intervention outcomes in southern Vietnam.

La Clinica terapeutica·2026
Same author

Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction.

IEEE transactions on computational imaging·2025
Same author

Impact of green credit policy on the operation of Vietnamese commercial banks: An empirical study using difference-in-differences model.

PloS one·2025

Related Experiment Video

Updated: Mar 8, 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

1.2K

Bounded Self-Weights Estimation Method for Non-Local Means Image Denoising Using Minimax Estimators.

Minh Phuong Nguyen, Se Young Chun

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 28, 2017
    PubMed
    Summary

    This study introduces novel local self-weight estimation methods (LMM-DB and LMM-RP) for Non-Local Means (NLM) filters, improving denoising performance and reducing artifacts in images. The new methods offer better bias-variance trade-offs and higher peak signal-to-noise ratios compared to existing techniques.

    Related Experiment Videos

    Last Updated: Mar 8, 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

    1.2K

    Area of Science:

    • Image Processing
    • Computer Vision
    • Signal Processing

    Background:

    • Non-local Means (NLM) filters offer powerful denoising and detail preservation.
    • Current methods like the local James-Stein (LJS) estimator for NLM self-weights can produce biased results due to unbounded estimates and large local areas.
    • Addressing limitations in NLM filter self-weight estimation is crucial for enhanced image denoising.

    Purpose of the Study:

    • To investigate and address the limitations of the local James-Stein (LJS) method in Non-Local Means (NLM) filter self-weight estimation.
    • To propose novel local self-weight estimation methods, LMM-DB and LMM-RP, based on Baranchik's minimax estimator.
    • To evaluate the performance of the proposed methods against classical NLM and LJS methods in terms of denoising quality and parameter selection.

    Main Methods:

    • Developed two novel local self-weight estimation methods: LMM-DB (direct bounds) and LMM-RP (reparametrization).
    • Both methods are based on Baranchik's minimax estimator, incorporating upper bounds to mitigate bias.
    • Evaluated methods on natural images and clinical MRI data with varying levels of additive Gaussian noise.

    Main Results:

    • The proposed LMM-DB and LMM-RP methods demonstrated an improved bias-variance trade-off compared to NLM and LJS.
    • Achieved higher peak signal-to-noise (PSNR) ratios and reduced visual artifacts in denoised images.
    • Provided a heuristic approach for selecting global smoothing parameters, yielding near-optimal PSNR values.

    Conclusions:

    • The novel LMM-DB and LMM-RP methods effectively address limitations in LJS self-weight estimation for NLM filters.
    • These methods offer superior denoising performance, characterized by better bias-variance balance and higher PSNR.
    • The proposed techniques provide a practical and effective way to enhance image denoising quality and select optimal parameters.