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...
Blind Procedures02:07

Blind Procedures

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was...
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
Convolution Properties II01:17

Convolution Properties II

The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...

You might also read

Related Articles

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

Sort by
Same author

Interaction in vivo and in vitro of the metastasis-inducing S100 protein, S100A4 (p9Ka) with S100A1.

The Journal of biological chemistry·2001
Same author

Hyperactive tendon reflexes in spastic multiple sclerosis: measures and mechanisms of action.

Archives of physical medicine and rehabilitation·2000
Same author

Synthesis of unsaturated carboacyclic nucleoside analogues via Mitsunobu reactions.

Nucleosides, nucleotides & nucleic acids·2000
Same author

Synthesis and cytokine modulation properties of pyrrolo[2, 3-d]-4-pyrimidone nucleosides.

Journal of medicinal chemistry·2000
Same author

Reciprocal information flow between prefrontal cortex and ventral tegmental area in an animal model of schizophrenia.

Neuroreport·2000
Same author

The "waiting period" of sensory and motor axons in early chick hindlimb: its role in axon pathfinding and neuronal maturation.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2000

Related Experiment Video

Updated: May 24, 2026

Live Images of GLUT4 Protein Trafficking in Mouse Primary Hypothalamic Neurons Using Deconvolution Microscopy
08:47

Live Images of GLUT4 Protein Trafficking in Mouse Primary Hypothalamic Neurons Using Deconvolution Microscopy

Published on: December 7, 2017

Development of blind image deconvolution and its applications.

M Jiang1, G Wang

  • 1School of Mathematical Sciences, Peking University, Beijing 100871, China.

Journal of X-Ray Science and Technology
|March 6, 2012
PubMed
Summary

This study updates blind image deconvolution methods, focusing on Gaussian blind deconvolution. It addresses limitations of previous techniques that required irreducible point spread functions (PSFs), expanding applications in medical imaging.

Related Experiment Videos

Last Updated: May 24, 2026

Live Images of GLUT4 Protein Trafficking in Mouse Primary Hypothalamic Neurons Using Deconvolution Microscopy
08:47

Live Images of GLUT4 Protein Trafficking in Mouse Primary Hypothalamic Neurons Using Deconvolution Microscopy

Published on: December 7, 2017

Area of Science:

  • Image processing
  • Computational imaging
  • Medical imaging analysis

Background:

  • Existing blind deconvolution methods often assume irreducible point spread functions (PSFs) and images.
  • This assumption limits applicability in scenarios with common PSFs like Gaussian.
  • Recent advancements are needed to overcome these limitations.

Purpose of the Study:

  • To supplement and update existing reviews on blind image deconvolution.
  • To highlight recent developments, particularly in Gaussian blind deconvolution.
  • To explore the clinical applications of advanced deconvolution techniques.

Main Methods:

  • Review of existing blind image deconvolution algorithms.
  • Focus on methods applicable to Gaussian point spread functions (PSFs).
  • Analysis of recent advancements and their performance.

Main Results:

  • Identified limitations of irreducibility assumptions in prior blind deconvolution methods.
  • Demonstrated the significance of Gaussian blind deconvolution for practical imaging systems.
  • Outlined recent progress in the field.

Conclusions:

  • Gaussian blind deconvolution offers a more robust approach for many imaging systems.
  • The findings support broader clinical applications of advanced deconvolution techniques.
  • This work provides an updated perspective on blind image deconvolution.