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

Curvilinear Motion: Normal and Tangential Components01:27

Curvilinear Motion: Normal and Tangential Components

When a car traverses a curved road, its motion can be elucidated by breaking it down into tangential and normal components. The car-centric coordinates attached to the vehicle move with it.
The positive direction of the t-axis aligns with the increasing position of the car along the curved path, denoted by the unit vector ut. Simultaneously, the n-axis, perpendicular to the t-axis, dissects the curved path into differential arc segments, each forming the arc of a circle with a radius of...
Deconvolution01:20

Deconvolution

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...
Degree of Curvature and Radius of Curvature01:19

Degree of Curvature and Radius of Curvature

The degree of curvature and the radius of curvature are fundamental concepts in determining the sharpness or smoothness of a curve. The degree of curvature is a measure of how steeply a curve bends and can be determined using the chord basis or the arc basis. In the chord basis method, the degree of curvature is defined as the central angle subtended by a chord of 30.48 meters, helping in the calculation of the radius of the curve. The arc basis method defines the degree of curvature as the...
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the time...
Curve Sketching and Derivatives01:22

Curve Sketching and Derivatives

Understanding the behavior of a function through its first and second derivatives is essential for analyzing its graph. Derivatives provide insight into where a function increases or decreases, where it attains local maxima or minima, and how its curvature behaves across different intervals.The first derivative of a function reveals the slope of the tangent line at any given point. Points where the derivative is zero or undefined are considered critical, as they often indicate potential extrema...
Reducing Line Loss01:18

Reducing Line Loss

In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...

You might also read

Related Articles

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

Sort by
Same author

Mechanism of organic-driven homogeneous nucleation transition for Ca-organics co-removal in oil drilling wastewater reclamation.

Water research·2026
Same author

Tissue-specific changes in endophytic bacterial and fungal communities of two pine species associated with pine wilt disease.

Frontiers in microbiology·2026
Same author

Leaf Age-Dependent Volatile Cues Influence Host Location and Oviposition Preference of <i>Obolodiplosis robiniae</i> on <i>Robinia pseudoacacia</i>.

Insects·2026
Same author

How does AI literacy empower decision-making quality? The mediating role of employee-AI collaboration and the moderating role of AI trust.

Acta psychologica·2026
Same author

Comprehensive antiphospholipid antibody profiling and unsupervised immune phenotyping in fetal growth restriction.

Frontiers in immunology·2026
Same author

HDAC8-selective inhibitor PCI-34051 protects against aortic dissection by attenuating ferroptosis of vascular smooth muscle cells.

Life medicine·2026
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

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

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

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

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

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

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

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

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

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

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

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

Related Experiment Video

Updated: Jun 7, 2026

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

Graph cuts for curvature based image denoising.

Egil Bae1, Juan Shi, Xue-Cheng Tai

  • 1Department of Mathematics, University of Bergen, 5020 Bergen, Norway. Egil.Bae@math.uib.no

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|November 5, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient graph cut algorithm for the Euler's elastica model in image denoising. The new method effectively reduces noise while preserving image details better than total variation models.

Related Experiment Videos

Last Updated: Jun 7, 2026

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Mathematics

Background:

  • Total Variation (TV) minimization is a standard image denoising technique.
  • TV models can produce staircasing artifacts.
  • Higher-order models like Euler's elastica offer improved results but are computationally complex.

Purpose of the Study:

  • To develop an efficient graph cut-based minimization algorithm for the Euler's elastica model.
  • To overcome the computational complexity of traditional methods for higher-order image denoising models.
  • To demonstrate the effectiveness of the proposed algorithm in image denoising.

Main Methods:

  • Formulating the Euler's elastica energy minimization as a sequence of graph representable problems.
  • Utilizing graph cuts for efficient combinatorial optimization.
  • Connecting the algorithm to the gradient flow of the energy function for guaranteed convergence.

Main Results:

  • The proposed algorithm efficiently minimizes the Euler's elastica energy.
  • Numerical experiments show superior performance compared to TV models.
  • The method achieves better preservation of sharp image features.

Conclusions:

  • The novel graph cut approach provides an efficient and effective solution for Euler's elastica-based image denoising.
  • This method overcomes limitations of traditional TV models, offering smoother results and better feature preservation.