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 Experiment Videos

Efficient frequency-domain sample selection for recovering limited-support images.

Nicholas D Blakeley1, P J Bones, R P Millane

  • 1Department of Electrical and Computer Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.

Journal of the Optical Society of America. A, Optics, Image Science, and Vision
|January 25, 2003
PubMed
Summary

A new algorithm efficiently finds optimal sampling patterns for image reconstruction from limited data. This method significantly speeds up the process while maintaining high accuracy in reconstructed images.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Reservoir computing using networks of memristors: effects of topology and heterogeneity.

Nanoscale·2023
Same author

Reservoir computing with 3D nanowire networks.

Neural networks : the official journal of the International Neural Network Society·2022
Same author

Geometric frustration in the myosin superlattice of vertebrate muscle.

Journal of the Royal Society, Interface·2021
Same author

Wavefront prediction with reservoir computing for minimizing the effects of angular anisoplanatism.

Applied optics·2018
Same author

Preliminary experimental results from a MARS Micro-CT system.

Journal of X-ray science and technology·2012
Same author

Zero tracks for blind deconvolution of blurred ensembles.

Applied optics·2010

Area of Science:

  • Signal Processing
  • Image Reconstruction
  • Computational Imaging

Background:

  • Image reconstruction from limited samples is challenging.
  • Determining stable sampling sets is computationally intensive.

Purpose of the Study:

  • Develop a faster algorithm for determining periodic nonuniform sampling patterns.
  • Improve the efficiency of image reconstruction from subsets of Nyquist samples.

Main Methods:

  • Sequential selection algorithm
  • Heuristic metrics for sampling set quality
  • Fast computation of sampling set metrics

Main Results:

  • Algorithm is orders of magnitude faster than existing methods.

Related Experiment Videos

  • Achieved comparable accuracy to slower, more rigorous algorithms.
  • Demonstrated effectiveness through simulations.

    • Conclusions:

    • The new algorithm provides a computationally efficient approach to image reconstruction.
    • Periodic nonuniform sampling patterns can be effectively determined.
    • This method offers a practical solution for stable image reconstruction from sparse data.