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Wavelet-Based Map Image Denoising Using Provably Better Class of Stochastic I.I.D. Image Models

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Author(s): Andriy Synyavskyy | Sviatoslav Voloshynovskiy | Ivan Prudyus

Journal: Facta Universitatis Series : Electronics and Energetics
ISSN 0353-3670

Volume: 14;
Issue: 3;
Start page: 375;
Date: 2001;
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Keywords: Wavelet | image model | image denoising | maximum posterior estimation | Student distribution | shrinkage function

ABSTRACT
The paper advocates a statistical approach to image denoising based on a Maximum a Posteriori (MAP) estimation in wavelet domain. In this framework, a new class of independent identically distributed (i.i.d.) stochastic image priors is considered to obtain a simple and tractable solution in a close analytical form. The proposed prior model is considered in the form of Student distribution. The experimental results demonstrate the high fidelity of this model for approximation of the sub-band distributions of wavelet coefficients. The obtained solution is presented in the form of well-studied shrinkage functions.
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