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

Maximum likelihood parametric blur identification based on a continuous spatial domain model.

G Pavlovic1, A M Tekalp

  • 1Dept. of Electr. Eng., Rochester Univ., NY.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1992
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

Genome engineering with Cas9 and AAV repair templates, successes and pitfalls.

Mammalian genome : official journal of the International Mammalian Genome Society·2025
Same author

Genome Wide Conditional Mouse Knockout Resources.

Drug discovery today. Disease models·2024
Same author

Importing genetically altered animals: ensuring quality.

Mammalian genome : official journal of the International Mammalian Genome Society·2021
Same author

Peri-operative massive pulmonary embolism management: is veno-arterial ECMO a therapeutic option?

Acta anaesthesiologica Scandinavica·2014
Same author

Which factors have an impact on occurrence, clinical and echocardiographic characteristics of idiopathic chordae rupture?

Acta clinica Belgica·2012
Same author

Proposal for a mesoscopic optical berry-phase interferometer.

Physical review letters·2009
Same journal

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Semantic Frame Interpolation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
See all related articles

This study introduces a new maximum-likelihood (ML) method for identifying image blur using continuous spatial coordinates. This approach accurately estimates blur parameters for various point spread functions, improving image deblurring accuracy.

Area of Science:

  • Image processing
  • Computer vision
  • Signal processing

Background:

  • Image blur degrades visual quality and complicates analysis.
  • Existing maximum-likelihood (ML) blur identification methods often rely on discrete spatial domain models.
  • These discrete models can limit the accurate estimation of blur parameters, especially for complex point spread functions.

Purpose of the Study:

  • To propose a novel formulation for maximum-likelihood (ML) blur identification.
  • To enable the estimation of blur extent and other parameters for arbitrary point spread functions (PSFs).
  • To utilize parametric modeling of blur in continuous spatial coordinates for improved accuracy.

Main Methods:

  • Developed a maximum-likelihood (ML) formulation for blur identification.

Related Experiment Videos

  • Employed parametric modeling of the blur in continuous spatial coordinates.
  • Derived a method to find the ML estimate of PSF parameters, including extent.

    • Main Results:

    • The proposed method successfully identifies blur using continuous spatial coordinates.
    • Accurate estimation of blur parameters was achieved for various PSFs, including those admitting closed-form parametric descriptions.
    • Experimental results demonstrated effectiveness for 1-D uniform motion blur, 2-D out-of-focus blur, and 2-D truncated Gaussian blur across different signal-to-noise ratios.

    Conclusions:

    • The proposed continuous-domain ML formulation offers a significant advancement over discrete-domain methods for blur identification.
    • This approach allows for more precise estimation of blur parameters, leading to better image deblurring.
    • The method is versatile and effective for a range of common and complex image blurs.