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

Single-frame multichannel blind deconvolution by nonnegative matrix factorization with sparseness constraints.

Ivica Kopriva1

  • 1Department of Electrical and Computer Engineering, The George Washington University, Washington, DC 20052, USA.

Optics Letters
|December 14, 2005
PubMed
Summary
This summary is machine-generated.

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

A hyperspectral imaging dataset and Grassmann manifold method for intraoperative pixel-wise classification of metastatic colon cancer in the liver.

Computers in biology and medicine·2025
Same author

Low tensor train and low multilinear rank approximations of 3D tensors for compression and de-speckling of optical coherence tomography images.

Physics in medicine and biology·2023
Same author

Non-negative Least Squares Approach to Quantification of <sup>1</sup>H Nuclear Magnetic Resonance Spectra of Human Urine.

Analytical chemistry·2020
Same author

Library-assisted nonlinear blind separation and annotation of pure components from a single <sup>1</sup>H nuclear magnetic resonance mixture spectra.

Analytica chimica acta·2019
Same author

l<sub>0</sub> -Motivated Low-Rank Sparse Subspace Clustering.

IEEE transactions on cybernetics·2018
Same author

Single-Channel Sparse Non-Negative Blind Source Separation Method for Automatic 3-D Delineation of Lung Tumor in PET Images.

IEEE journal of biomedical and health informatics·2016

This study introduces a novel blind deconvolution method using Gabor filters for sparse image representation. The approach effectively reconstructs images without prior knowledge of blur or source, demonstrating strong experimental results.

Area of Science:

  • Image Processing
  • Computer Vision
  • Signal Processing

Background:

  • Blind deconvolution is crucial for image restoration but challenging due to unknown blur kernels and source images.
  • Existing methods often require prior knowledge or iterative estimations, limiting their applicability.

Purpose of the Study:

  • To develop a single-frame multichannel blind deconvolution algorithm.
  • To leverage Gabor filter properties for sparse image representation in deconvolution.

Main Methods:

  • Formulated blind deconvolution using a bank of Gabor filters applied to a blurred image.
  • Represented the multichannel image as a product of sparse mixing vectors and source images.
  • Framed the problem as nonnegative matrix factorization with a sparseness constraint.

Related Experiment Videos

Main Results:

  • Achieved effective blind deconvolution without requiring a priori knowledge of the blurring kernel or original image.
  • Demonstrated the viability of the proposed concept through positive experimental outcomes.

Conclusions:

  • The proposed Gabor filter-based nonnegative matrix factorization method offers a robust solution for single-frame multichannel blind deconvolution.
  • This approach enhances image deconvolution by exploiting image sparseness and eliminating the need for prior information.