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

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.4K
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...
8.4K
Variation01:19

Variation

7.5K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
7.5K
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

3.6K
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...
3.6K
Degree of Curvature and Radius of Curvature01:19

Degree of Curvature and Radius of Curvature

315
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...
315
Differential Leveling01:12

Differential Leveling

483
Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
483
Deconvolution01:20

Deconvolution

404
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...
404

You might also read

Related Articles

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

Sort by
Same author

MAP Image Recovery with Guarantees using Locally Convex Multi-Scale Energy (LC-MUSE) Model.

Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)·2026
Same author

MEMORY-EFFICIENT DEEP END-TO-END POSTERIOR NETWORK (DEEPEN) FOR INVERSE PROBLEMS.

Proceedings. IEEE International Symposium on Biomedical Imaging·2025
Same author

ACCELERATING QUANTITATIVE MRI USING SUBSPACE MULTISCALE ENERGY MODEL (SS-MUSE).

Proceedings. IEEE International Symposium on Biomedical Imaging·2025
Same author

FAST MULTI-CONTRAST MRI USING JOINT MULTISCALE ENERGY MODEL.

Proceedings. IEEE International Symposium on Biomedical Imaging·2025
Same author

Accelerating 3D radial MPnRAGE using a self-supervised deep factor model.

Magnetic resonance in medicine·2025
Same author

Multi-Scale Energy (MuSE) framework for inverse problems in imaging.

IEEE transactions on computational imaging·2025
Same journal

SPARSITY-DRIVEN PARALLEL IMAGING CONSISTENCY FOR IMPROVED SELF-SUPERVISED MRI RECONSTRUCTION.

Proceedings. International Conference on Image Processing·2026
Same journal

MULTIMODAL CELL CONTEXT INSTRUCTION TUNING FOR CONDITIONAL DNA REGULATORY SEQUENCE GENERATION WITH LARGE LANGUAGE MODELS.

Proceedings. International Conference on Image Processing·2025
Same journal

LOCALIZING MOMENTS OF ACTIONS IN UNTRIMMED VIDEOS OF INFANTS WITH AUTISM SPECTRUM DISORDER.

Proceedings. International Conference on Image Processing·2025
Same journal

Learning From PU Data Using Disentangled Representations.

Proceedings. International Conference on Image Processing·2025
Same journal

DISCO: A DIFFUSION MODEL FOR SPATIAL TRANSCRIPTOMICS DATA COMPLETION.

Proceedings. International Conference on Image Processing·2025
Same journal

A PHYSICS-GUIDED SMOOTHING METHOD FOR MATERIAL MODELING WITH DIGITAL IMAGE CORRELATION (DIC) MEASUREMENTS.

Proceedings. International Conference on Image Processing·2025
See all related articles

Related Experiment Video

Updated: Nov 16, 2025

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation
07:50

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

Published on: January 27, 2023

3.2K

Multiple Degree Total Variation (MDTV) Regularization for Image Restoration.

Yue Hu1, Mathews Jacob2

  • 1School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China.

Proceedings. International Conference on Image Processing
|February 25, 2021
PubMed
Summary
This summary is machine-generated.

We developed multiple degree total variation (MDTV) for image processing. This novel method balances edge preservation and smoothness, outperforming existing techniques in image denoising and compressed sensing for better image recovery.

More Related Videos

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline
09:27

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

7.3K
Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
07:03

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

7.9K

Related Experiment Videos

Last Updated: Nov 16, 2025

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation
07:50

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

Published on: January 27, 2023

3.2K
Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline
09:27

Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

Published on: January 30, 2019

7.3K
Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration
07:03

Medical-grade Sterilizable Target for Fluid-immersed Fetoscope Optical Distortion Calibration

Published on: February 23, 2017

7.9K

Area of Science:

  • Image processing and computer vision
  • Mathematical optimization
  • Signal and image recovery

Background:

  • Image regularization is crucial for restoring degraded images.
  • Existing methods like Total Variation (TV) offer good edge preservation but can oversmooth regions.
  • Higher-order methods improve smoothness but may blur important image features.

Purpose of the Study:

  • Introduce a novel image regularization technique, Multiple Degree Total Variation (MDTV).
  • To achieve a superior balance between edge preservation and region smoothness in image reconstruction.
  • To demonstrate MDTV's effectiveness in image denoising and compressed sensing applications.

Main Methods:

  • Developed MDTV regularization by combining first and second-degree directional derivatives.
  • Proposed a fast majorize-minimize algorithm to efficiently solve the resulting optimization problem.
  • Evaluated MDTV against standard TV, Higher Degree Total Variation (HDTV), and Total Generalized Variation (TGV) methods.

Main Results:

  • MDTV demonstrated a favorable balance between preserving image edges and smoothing flat regions.
  • The proposed majorize-minimize algorithm provided efficient optimization for MDTV.
  • Numerical results showed MDTV achieved superior image recovery performance compared to benchmark methods.

Conclusions:

  • MDTV is an effective image regularization method offering improved performance.
  • The technique shows significant promise for image denoising and compressed sensing.
  • MDTV represents a valuable advancement in image reconstruction algorithms.