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

Relative focus map estimation using blind deconvolution.

Levente Kovács1, Tamás Szirányi

  • 1Department of Image Processing and Neurocomputing, University of Veszprém, Hungary. kla@vision.vein.hu

Optics Letters
|December 1, 2005
PubMed
Summary

This study introduces an automatic focus map extraction method using modified blind deconvolution to estimate localized blurring functions (point-spread functions). This approach efficiently identifies focus areas in images without needing camera or optical system details.

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

Noninvasive and minimally invasive approaches for acute and chronic stress assessment in dairy cattle.

Journal of dairy science·2026
Same author

The value of social robots supporting informal care: a discrete choice experiment among informal caregivers.

The European journal of health economics : HEPAC : health economics in prevention and care·2026
Same author

Stochastic virtual population in type 1 diabetes.

PloS one·2026
Same author

Improving the value of population health data for health policy and decision-making using machine learning algorithms in EQ-5D-5L index estimation.

Scientific reports·2026
Same author

Bioinformatics-Inspired IMU Stride Sequence Modeling for Fatigue Detection Using Spectral-Entropy Features and Hybrid AI in Performance Sports.

Sensors (Basel, Switzerland)·2026
Same author

Algorithm-assisted individualized therapy design improves survival in a mouse model of triple-negative breast cancer.

NPJ precision oncology·2026

Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Imaging

Background:

  • Accurate focus estimation is crucial for image quality and analysis.
  • Traditional methods often require specific imaging conditions or system knowledge.
  • Blind deconvolution techniques offer potential for estimating image blur without prior information.

Purpose of the Study:

  • To develop an automatic method for extracting focus maps from ordinary images.
  • To estimate localized blurring functions, known as point-spread functions (PSFs), for focus area identification.
  • To create a focus estimation technique that is robust to noise and ill-posed deconvolution problems.

Main Methods:

  • A modified blind deconvolution approach is employed.
  • Localized blurring functions (point-spread functions or PSFs) are estimated.

Related Experiment Videos

  • The method focuses on estimating PSFs and a relative focus map, rather than full image reconstruction.

    • Main Results:

    • The developed method successfully extracts focus maps from ordinary images.
    • Localized PSFs are estimated effectively, enabling focus area identification.
    • The technique demonstrates reduced sensitivity to noise and ill-posed deconvolution challenges compared to general deconvolution.

    Conclusions:

    • The proposed automatic focus map extraction method is effective and robust.
    • It enables focus area estimation without prior knowledge of shooting conditions or optical systems.
    • This technique offers a valuable tool for image analysis and processing applications.