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

Hidden Markov models for wavelet-based blind source separation.

Mahieddine M Ichir1, Ali Mohammad-Djafari

  • 1Laboratoire des signaux et systèmes (CNRS-Supélec-UPS) Supélec, Gif-Sur-Yvette, France. ichir@lss.supelec.fr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|July 13, 2006
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

Physics-Informed Neural Networks with Unknown Partial Differential Equations: An Application in Multivariate Time Series.

Entropy (Basel, Switzerland)·2025
Same author

Variational Bayesian Approximation (VBA): Implementation and Comparison of Different Optimization Algorithms.

Entropy (Basel, Switzerland)·2024
Same author

Dynamical system identification, model selection, and model uncertainty quantification by Bayesian inference.

Chaos (Woodbury, N.Y.)·2024
Same author

An interpretable machine learning approach to study the relationship beetwen retrognathia and skull anatomy.

Scientific reports·2023
Same author

Regularization, Bayesian Inference, and Machine Learning Methods for Inverse Problems.

Entropy (Basel, Switzerland)·2021
Same author

Bayesian 3D X-ray Computed Tomography with a Hierarchical Prior Model for Sparsity in Haar Transform Domain.

Entropy (Basel, Switzerland)·2020
Same journal

Mask-guided Asymmetric Contrastive and Semantic Alignment for Unsupervised Person Re-Identification.

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

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

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

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

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

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

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

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

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

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

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

This study introduces a Bayesian framework for blind source separation in the wavelet domain, utilizing advanced models like hidden Markov trees for improved signal recovery and denoising, even with high noise levels.

Area of Science:

  • Signal Processing
  • Statistical Inference
  • Wavelet Theory

Background:

  • Blind source separation (BSS) is crucial for isolating individual signals from mixed data.
  • Traditional BSS methods often struggle with noisy data and complex signal structures.
  • Wavelet domain analysis offers advantages for signal decomposition and feature extraction.

Purpose of the Study:

  • To develop a robust Bayesian framework for blind source separation in the wavelet domain.
  • To evaluate different statistical models for wavelet coefficients in BSS.
  • To enhance separation and denoising performance, particularly in high noise conditions.

Main Methods:

  • Proposed a Bayesian estimation framework for wavelet coefficients.
  • Investigated independent Gaussian mixture, hidden Markov tree, and contextual hidden Markov field models.

Related Experiment Videos

  • Developed Markov chain Monte Carlo (MCMC) algorithms for unsupervised joint separation and parameter estimation.
  • Introduced a Bernoulli-Gaussian mixture model for enhanced denoising.

    • Main Results:

    • Demonstrated the effectiveness of the Bayesian approach across various signal-to-noise ratios (SNRs).
    • Showcased improved performance with hidden Markov tree and contextual hidden Markov field models compared to simpler models.
    • Validated the joint separation and denoising capabilities, especially for high noise levels.
    • Evaluated the impact of different wavelet basis choices on separation accuracy.

    Conclusions:

    • The proposed Bayesian framework provides a powerful tool for blind source separation in the wavelet domain.
    • The choice of wavelet coefficient model significantly impacts BSS performance.
    • The modified Bernoulli-Gaussian model effectively integrates denoising with separation.