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

Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

3.4K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
3.4K
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

167
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
167
Deconvolution01:20

Deconvolution

374
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...
374
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.2K
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...
8.2K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

180
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
180
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

224
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
224

You might also read

Related Articles

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

Sort by
Same author

Improved NMR-data-compliant protein structure modeling captures context-dependent variations and expands the scope of functional inference.

Proteins·2022
Same author

Plug-and-Play Regularization Using Linear Solvers.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2022
Same author

Fast High-Dimensional Bilateral and Nonlocal Means Filtering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2018
Same author

Fast Adaptive Bilateral Filtering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2018
Same author

Generalized Semantic Preserving Hashing for Cross-Modal Retrieval.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2018
Same author

Fast and Provably Accurate Bilateral Filtering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2016

Related Experiment Video

Updated: Nov 7, 2025

Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software
08:22

Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software

Published on: July 17, 2018

7.5K

On Plug-and-Play Regularization Using Linear Denoisers.

Ruturaj G Gavaskar, Chirayu D Athalye, Kunal N Chaudhury

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |April 28, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Plug-and-play (PnP) regularization combines forward models with denoisers for image reconstruction. This study proves a broader class of denoisers can be optimized, ensuring better image restoration convergence.

    More Related Videos

    Characterization of Anisotropic Leaky Mode Modulators for Holovideo
    09:36

    Characterization of Anisotropic Leaky Mode Modulators for Holovideo

    Published on: March 19, 2016

    8.1K

    Related Experiment Videos

    Last Updated: Nov 7, 2025

    Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software
    08:22

    Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software

    Published on: July 17, 2018

    7.5K
    Characterization of Anisotropic Leaky Mode Modulators for Holovideo
    09:36

    Characterization of Anisotropic Leaky Mode Modulators for Holovideo

    Published on: March 19, 2016

    8.1K

    Area of Science:

    • Image processing
    • Computational imaging
    • Optimization

    Background:

    • Plug-and-play (PnP) regularization is a powerful technique for state-of-the-art image reconstruction.
    • PnP combines forward models with powerful denoisers within proximal algorithms.
    • Current PnP methods face limitations with non-symmetric denoisers like kernel methods.

    Purpose of the Study:

    • To investigate the optimality of PnP iterations in minimizing a combined loss and regularizer function (f+g).
    • To determine if a broader class of linear denoisers can be represented as proximal maps.
    • To develop modified PnP algorithms ensuring convergence and improved image restoration.

    Main Methods:

    • Proving that linear denoisers, including symmetric and kernel-based ones, can be expressed as proximal maps of convex regularizers.
    • Analyzing the theoretical underpinnings of PnP algorithms for optimality.
    • Modifying PnP update steps for non-symmetric denoisers.

    Main Results:

    • Demonstrated that a wider range of linear denoisers can be formulated as proximal maps.
    • Established that non-symmetric denoisers necessitate specific algorithmic modifications in PnP updates.
    • Showcased that modified PnP algorithms guarantee convergence to the minimum of f+g.

    Conclusions:

    • The theoretical framework for PnP regularization is extended to include non-symmetric denoisers.
    • Modified PnP algorithms ensure convergence and yield high-quality image restorations.
    • This work provides a theoretical foundation for optimizing PnP-based image reconstruction techniques.