Wavelet-based image estimation: an empirical Bayes approach using Jeffrey's noninformative prior

The sparseness and decorrelation properties of the discrete wavelet transform have been exploited to develop powerful denoising methods. However, most of these methods have free parameters which have to be adjusted or estimated. In this paper, we propose a wavelet-based denoising technique without a...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on image processing 2001-09, Vol.10 (9), p.1322-1331
Main Authors: Figueiredo, M.A.T., Nowak, R.D.
Format: Article
Language:English
Subjects:
Adjustment
Applied sciences
Bayesian analysis
Bayesian methods
Decorrelation
Discrete Wavelet Transform
Discrete wavelet transforms
Empirical analysis
Exact sciences and technology
Image analysis
Image coding
Image denoising
Image processing
Information, signal and communications theory
Noise reduction
Nonlinearity
Shrinkage
Signal analysis
Signal processing
Telecommunications and information theory
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The sparseness and decorrelation properties of the discrete wavelet transform have been exploited to develop powerful denoising methods. However, most of these methods have free parameters which have to be adjusted or estimated. In this paper, we propose a wavelet-based denoising technique without any free parameters; it is, in this sense, a "universal" method. Our approach uses empirical Bayes estimation based on a Jeffreys' noninformative prior; it is a step toward objective Bayesian wavelet-based denoising. The result is a remarkably simple fixed nonlinear shrinkage/thresholding rule which performs better than other more computationally demanding methods.
ISSN:1057-7149
1941-0042
DOI:10.1109/83.941856