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

Approximate maximum likelihood hyperparameter estimation for Gibbs priors.

Z Zhou1, R N Leahy, J Qi

  • 1Dept. of Electr. Eng. Syst., Univ. of Southern California, Los Angeles, CA.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1997
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

Challenging Spontaneous Quantum Collapse with the XENONnT Dark Matter Detector.

Physical review letters·2026
Same author

WIMP Dark Matter Search Using a 3.1 Tonne-Year Exposure of the XENONnT Experiment.

Physical review letters·2025
Same author

Short-term outcomes of endoscopic intermuscular dissection for early rectal cancer with deep submucosal infiltration: a single-center experience from China.

Techniques in coloproctology·2025
Same author

Targeting NLRP3/caspase-1/GSDMD to treat inner ear injury from labyrinthine hemorrhage.

Molecular therapy : the journal of the American Society of Gene Therapy·2025
Same author

Search for Light Dark Matter in Low-Energy Ionization Signals from XENONnT.

Physical review letters·2025
Same author

[Structural equation analysis and modeling of upper limb WMSDs and their adverse ergonomic factors].

Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases·2025
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
Same journal

GoP-based Quality Enhancement on Video Compression.

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

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

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

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

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

This study introduces an approximate maximum likelihood (ML) estimator for the global hyperparameter beta in Bayesian image estimation with Gibbs priors. This method simplifies complex calculations for image restoration and reconstruction from incomplete data.

Area of Science:

  • Computational imaging
  • Statistical modeling
  • Image processing

Background:

  • Bayesian image estimation relies on prior parameters (hyperparameters), especially the global hyperparameter beta in Gibbs priors.
  • Maximum likelihood (ML) estimation of beta from incomplete data is crucial for applications like image restoration and reconstruction but is often intractable.
  • Existing methods face challenges with exact ML estimation due to the complexity of Gibbs priors and indirect observations.

Purpose of the Study:

  • To develop an approximate ML estimator for the global hyperparameter beta in Bayesian image estimation.
  • To enable tractable estimation of beta from incomplete or degraded image data.
  • To integrate this estimation process with Maximum A Posteriori (MAP) image estimation.

Main Methods:

Related Experiment Videos

  • Utilized a mean field approximation to simplify multidimensional Gibbs distributions into separable 1-D densities.
  • Developed an approximate ML estimator computed concurrently with a MAP image estimate.
  • Applied the Gibbs-Bogoliubov-Feynman (GBF) bound for optimizing the approximation in specific scenarios.

Main Results:

  • Demonstrated a method to simplify the ML estimation of beta using mean field approximation.
  • Showcased the simultaneous computation of approximate ML beta estimation and MAP image estimation.
  • Presented Monte Carlo simulation results evaluating the bias and variance of the proposed estimator in image restoration.

Conclusions:

  • The proposed approximate ML estimator offers a viable solution for estimating the global hyperparameter beta in Bayesian image processing with Gibbs priors.
  • The mean field approximation effectively simplifies intractable estimation problems, making them computationally feasible.
  • The simultaneous estimation approach is efficient for image restoration and reconstruction tasks involving incomplete data.