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

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...
Application of Nonlinear Inequalities01:29

Application of Nonlinear Inequalities

A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality:  can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the key values are 3...
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
Newton’s Method01:30

Newton’s Method

Newton’s Method is a powerful iterative technique for approximating the roots of real-valued, differentiable functions, particularly when analytical solutions are impractical. This approach is widely used in scientific computing, engineering, and finance, where equations may be too complex for traditional algebraic methods to handle. The method relies on an iterative process that refines an initial estimate using the function’s derivative to approach the true solution progressively.
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Optimization Problems01:26

Optimization Problems

Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...

You might also read

Related Articles

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

Sort by
Same author

Geochemistry shapes microbial diversity and selected functional traits in flowback and produced waters from hydraulically fractured formations.

FEMS microbiology ecology·2026
Same author

Twin-Boundary Engineering in FeTe<sub>1-<i>x</i></sub>Se<sub><i>x</i></sub> Superconductor.

ACS nano·2026
Same author

Correction to "Lifting the Fog: Graphene Gas Cell for <i>In Situ</i> (Scanning) Transmission Electron Microscopy with Robust Single-Atom Sensitivity".

ACS nano·2026
Same author

<i>Helicobacter pylori</i> and hyperglycemia fuel gastric cancer glycolysis: Mechanisms and targeted intervention (Review).

International journal of molecular medicine·2026
Same author

Portable and dynamic magnetoencephalography measurement using compact MSR by precise two-stage magnetic field adjustment and control strategy.

NeuroImage·2026
Same author

Colossal infrared nonlinear optical anisotropy in a 2D charge-transfer Mott insulator.

Light, science & applications·2026
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

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

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

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

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

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

GoP-based Quality Enhancement on Video Compression.

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

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

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

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

Related Experiment Video

Updated: May 20, 2026

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
13:54

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Efficient algorithm for nonconvex minimization and its application to PM regularization.

Wen-Ping Li1, Zheng-Ming Wang, Ya Deng

  • 1Department of Mathematics and Systems Science, National University of Defense Technology, Changsha, China. www8422lwp@yahoo.com.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|July 26, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces efficient iterative schemes for nonconvex regularization in image processing, improving edge preservation and computation. The new algorithms offer better denoising, faster convergence, and higher precision for image restoration tasks.

Related Experiment Videos

Last Updated: May 20, 2026

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
13:54

A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)

Published on: August 18, 2023

Area of Science:

  • Image Processing
  • Computational Mathematics
  • Computer Vision

Background:

  • Nonconvex regularization in image processing effectively smooths regions and sharpens edges.
  • However, nonconvex regularization presents significant computational challenges.
  • Existing methods struggle with efficient minimization of nonconvex energy functions.

Purpose of the Study:

  • To propose novel iterative schemes for minimizing energy functions with nonconvex edge-preserving potentials.
  • To address the computational challenges associated with nonconvex regularization.
  • To enhance the efficiency and performance of image denoising algorithms.

Main Methods:

  • Development of iterative schemes derived from duality-based algorithms and fixed-point iteration.
  • Mathematical proof of convergence for convex energy functions with nonconvex potentials, including linear convergence rates.
  • Application of schemes to Perona and Malik's nonconvex regularization model.

Main Results:

  • Demonstration of efficient algorithms based on the proposed iterative schemes.
  • Experimental validation showing superior denoised performance compared to existing methods.
  • Quantified improvements in convergence speed, calculation precision, and computational cost.

Conclusions:

  • The proposed iterative schemes provide an efficient solution for nonconvex regularization in image processing.
  • The developed algorithms achieve better denoising results with reduced computational burden.
  • These advancements offer practical benefits for image restoration and analysis.