Wavelet Denoising of Multicomponent Images Using Gaussian Scale Mixture Models and a Noise-Free Image as Priors

In this paper, a Bayesian wavelet-based denoising procedure for multicomponent images is proposed. A denoising procedure is constructed that 1) fully accounts for the multicomponent image covariances, 2) makes use of Gaussian scale mixtures as prior models that approximate the marginal distributions...

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Bibliographic Details
Published in:IEEE transactions on image processing 2007-07, Vol.16 (7), p.1865-1872
Main Authors: Scheunders, P., De Backer, S.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Approximation
Artificial Intelligence
Bayesian analysis
Bayesian methods
Bayesian wavelet-based denoising
Biological and medical sciences
Biomedical imaging
Computer Simulation
Computerized, statistical medical data processing and models in biomedicine
Detection, estimation, filtering, equalization, prediction
Discrete wavelet transforms
Exact sciences and technology
Gaussian
Gaussian noise
Gaussian scale mixture model (GSM)
GSM
Hyperspectral imaging
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image processing
Information, signal and communications theory
Medical management aid. Diagnosis aid
Medical sciences
Models, Statistical
multimodal medical images
multispectral images
Noise reduction
Normal Distribution
Principal component analysis
Remote sensing
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Simulation
Stochastic Processes
Telecommunications and information theory
Wavelet
Wavelet coefficients
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Description
Summary:In this paper, a Bayesian wavelet-based denoising procedure for multicomponent images is proposed. A denoising procedure is constructed that 1) fully accounts for the multicomponent image covariances, 2) makes use of Gaussian scale mixtures as prior models that approximate the marginal distributions of the wavelet coefficients well, and 3) makes use of a noise-free image as extra prior information. It is shown that such prior information is available with specific multicomponent image data of, e.g., remote sensing and biomedical imaging. Experiments are conducted in these two domains, in both simulated and real noisy conditions.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2007.899598