Search research articles

Search research articles

Related Experiment Videos

A Bayesian framework for noise covariance estimation using the facet model.

Desikachari Nadadur¹, Robert Martin Haralick, David Earl Gustafson

¹Advanced Imaging Applications, Developing Competency Department, Siemens Medical Solutions USA Inc, Ultrasound Division, Issaquah, WA 98029, USA. desikachari.nadadur@siemens.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|November 11, 2005

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 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

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

This study introduces a novel Bayesian method to estimate correlated noise covariance in image processing, moving beyond traditional white noise assumptions. The new approach accurately models complex noise structures for improved data analysis.

Area of Science:

Image Processing
Statistical Modeling
Bayesian Inference

Background:

Traditional image processing assumes uncorrelated white noise (covariance sigma2I).
Limited research exists on estimating correlated noise characteristics.
This gap hinders accurate analysis in complex data scenarios.

Purpose of the Study:

To develop a novel Bayesian approach for simultaneous estimation of unknown colored/correlated noise covariance matrices.
To estimate hyperparameters of the covariance model using the facet model.
To address limitations of existing noise modeling techniques in image processing.

Main Methods:

Utilized the facet model for its mathematical simplicity and elegance.
Employed a generalized inverted Wishart density as the prior for the noise covariance matrix.

Related Experiment Videos

Developed a generalized constrained expectation-maximization algorithm for hyperparameter estimation.

Main Results:

Successfully proposed a new Bayesian framework for correlated noise estimation.
Demonstrated the capability to simultaneously estimate noise covariance and model hyperparameters.
The generalized constrained expectation-maximization algorithm provides an effective extension for complex estimation tasks.

Conclusions:

The proposed method offers a significant advancement over traditional white noise assumptions in image processing.
Accurate estimation of correlated noise is crucial for robust data analysis and model performance.
This work provides a powerful new tool for researchers dealing with complex noise structures.