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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

7.9K
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...
7.9K
Deconvolution01:20

Deconvolution

484
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...
484
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

603
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
603
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

325
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
325
Gaussian Elimination: Problem Solving01:30

Gaussian Elimination: Problem Solving

118
Systems of linear equations in several variables are pivotal in modeling complex scenarios involving multiple unknowns and constraints. Such systems are widely used in various fields to represent relationships where several conditions must be simultaneously satisfied. Each variable in the system corresponds to an unknown quantity, while each equation imposes a linear constraint, leading to a structured approach for analyzing and solving real-world problems.A system of three equations with three...
118
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.8K
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...
8.8K

You might also read

Related Articles

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

Sort by
Same author

Prevention of contrast-induced nephropathy with prostaglandin E1 in high-risk patients undergoing percutaneous coronary intervention.

International urology and nephrology·2014
Same author

Simple whole-cell biodetection and bioremediation of heavy metals based on an engineered lead-specific operon.

Environmental science & technology·2014
Same author

The dual PI3K/mTOR inhibitor NVP-BEZ235 prevents epithelial-mesenchymal transition induced by hypoxia and TGF-β1.

European journal of pharmacology·2014
Same author

The genus Anemarrhena Bunge: A review on ethnopharmacology, phytochemistry and pharmacology.

Journal of ethnopharmacology·2014
Same author

Aldehyde dehydrogenase-2 is a host factor required for effective bone marrow mesenchymal stem cell therapy.

Arteriosclerosis, thrombosis, and vascular biology·2014
Same author

Myocardial steatosis and its association with obesity and regional ventricular dysfunction: evaluated by magnetic resonance tagging and 1H spectroscopy in healthy African Americans.

International journal of cardiology·2014

Related Experiment Video

Updated: Dec 21, 2025

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

9.9K

Structured Dictionary Learning for Image Denoising under Mixed Gaussian and Impulse Noise.

Hong Zhu, Michael K Ng

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 15, 2020
    PubMed
    Summary

    This study introduces novel dictionary learning models for mixed noise denoising, effectively removing Gaussian and impulse noise. The proposed methods outperform existing techniques in image restoration quality.

    Related Experiment Videos

    Last Updated: Dec 21, 2025

    Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
    14:58

    Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

    Published on: June 2, 2010

    9.9K

    Area of Science:

    • Computer Vision
    • Image Processing
    • Machine Learning

    Background:

    • Image denoising is crucial for image restoration.
    • Mixed noise denoising, specifically Gaussian and impulse noise, remains a challenge.
    • Existing methods often struggle with combined noise types.

    Purpose of the Study:

    • To develop advanced dictionary learning models for mixed noise denoising.
    • To address the limitations of current methods in handling combined Gaussian and impulse noise.
    • To improve the quality of restored images corrupted by mixed noise.

    Main Methods:

    • Proposed two structured dictionary learning models.
    • Utilized ℓp-norm fidelity and ℓq-norm regularization for sparse coding.
    • Employed proximal (and proximal linearized) alternating minimization algorithms.
    • Addressed Gaussian noise via linear representation in an orthogonal basis.
    • Implemented distinct impulse noise removal strategies for each model.

    Main Results:

    • Demonstrated superior performance of the proposed denoising models.
    • Achieved better image quality assessment metrics compared to existing methods.
    • Validated the effectiveness of the structured dictionary learning approach for mixed noise.

    Conclusions:

    • The proposed dictionary learning models are effective for mixed Gaussian and impulse noise removal.
    • The ℓp-norm fidelity plus ℓq-norm regularization framework offers a robust solution.
    • This work advances the field of mixed noise image denoising.