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...
Boundary Conditions: Lossless Lines01:21

Boundary Conditions: Lossless Lines

Consider a single-phase, two-wire, lossless transmission line terminated by an impedance at the receiving end and a source with Thevenin voltage and impedance at the sending end. The line, with length, has a surge impedance and wave velocity determined by the line's inductance and capacitance.
At the receiving end, the boundary condition states that the voltage equals the product of the receiving-end impedance and current. This relationship is expressed as a function of the incident and...
Downsampling01:20

Downsampling

When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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...
Shrinkage in Concrete01:27

Shrinkage in Concrete

Shrinkage in concrete is primarily due to water loss from evaporation, hydration of cement, or carbonation, leading to a reduction in volume. The volumetric contraction results in volumetric strain in concrete. However, in practice, shrinkage is measured as linear strain, which is one-third of the volumetric strain.
When concrete is still in its plastic state, it can undergo a decrease in volume by about 1% of its absolute volume. This decrease is known as plastic shrinkage. It arises either...
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...

You might also read

Related Articles

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

Sort by
Same author

Spatially regularized estimation of the tissue homogeneity model parameters in DCE-MRI using proximal minimization.

Magnetic resonance in medicine·2019
Same author

Detection of copy-move image modification using JPEG compression model.

Forensic science international·2017
Same author

Fast Bayesian JPEG Decompression and Denoising With Tight Frame Priors.

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

PIZZARO: Forensic analysis and restoration of image and video data.

Forensic science international·2016
Same author

Restoration of retinal images with space-variant blur.

Journal of biomedical optics·2014
Same author

Platform motion blur image restoration system.

Applied optics·2012

Related Experiment Video

Updated: May 27, 2026

Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

Removing boundary artifacts for real-time iterated shrinkage deconvolution.

Michal Sorel

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |November 23, 2011
    PubMed
    Summary
    This summary is machine-generated.

    We introduce a novel solution to fix boundary artifacts in fast deblurring algorithms. This method, inspired by Wiener filter deconvolution, minimally increases computation time.

    Related Experiment Videos

    Last Updated: May 27, 2026

    Quantifying Intermembrane Distances with Serial Image Dilations
    07:45

    Quantifying Intermembrane Distances with Serial Image Dilations

    Published on: September 28, 2018

    Area of Science:

    • Image processing and computational imaging.
    • Signal processing and algorithm development.

    Background:

    • Fast deblurring algorithms often suffer from boundary artifacts.
    • Iterated shrinkage thresholding in sparse domains and Fourier domain deconvolution are common techniques.

    Discussion:

    • Boundary artifacts arise from limitations in current fast deblurring methods.
    • The proposed solution adapts Reeves' Wiener filter deconvolution concept.
    • Computational cost is managed, with computation time less than doubling.

    Key Insights:

    • A novel approach effectively mitigates boundary artifacts in sparse domain and Fourier deconvolution.
    • The method integrates Wiener filter principles for improved deblurring.
    • Efficiency is maintained with a manageable increase in processing time.

    Outlook:

    • Potential for broader application in real-time image restoration.
    • Further research could explore variations for different artifact types.
    • This work advances the field of efficient and artifact-free image deblurring.