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

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...
Differential Leveling01:12

Differential Leveling

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...

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

Related Experiment Video

Updated: May 25, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Higher degree total variation (HDTV) regularization for image recovery.

Yue Hu1, Mathews Jacob

  • 1Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14627, USA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 18, 2012
PubMed
Summary

We developed higher degree TV (HDTV) penalties to improve image reconstruction, reducing artifacts like staircasing and ringing. The anisotropic HDTV penalty offers superior performance over isotropic HDTV and traditional methods.

Related Experiment Videos

Last Updated: May 25, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

Area of Science:

  • Image Processing
  • Computer Vision
  • Applied Mathematics

Background:

  • Classical Total Variation (TV) regularization methods face practical limitations in image reconstruction.
  • Existing TV schemes exhibit staircase and ringing artifacts, and may struggle with singularity preservation.

Purpose of the Study:

  • To introduce novel image regularization penalties, termed higher degree TV (HDTV), to address limitations of classical TV.
  • To develop efficient algorithms for solving the optimization problems arising from these new penalties.

Main Methods:

  • Derived two families of functionals: isotropic and anisotropic HDTV penalties, utilizing higher degree partial image derivatives.
  • Isotropic penalty uses L(1)-L(2) mixed norm; anisotropic penalty uses separable L(1) norm of directional derivatives.
  • Developed efficient majorize-minimize algorithms for optimization problem solving.

Main Results:

  • HDTV penalties inherit desirable TV properties: rotation/translation invariance, discontinuity preservation, and convexity.
  • Anisotropic HDTV enhances linelike features and preserves discontinuities, outperforming isotropic HDTV and classical TV.
  • Proposed algorithms effectively minimize staircase and ringing artifacts, offering improved image reconstruction quality.

Conclusions:

  • The novel anisotropic HDTV penalty provides superior image reconstruction compared to isotropic HDTV, classical TV, and wavelet methods.
  • HDTV regularization effectively reduces common artifacts while preserving image singularities.
  • Efficient majorize-minimize algorithms enable practical application of HDTV penalties.