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Title

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

Authors
Synyavskyy, A.
Prudyus, I.
Published in Facta Universitatis - Series: Electronics and Energetics. 2001, vol. 14
Abstract The paper advocates a statistical approach to image denoising based on a maximum a posteriori (MAP) estimation in the wavelet domain. In this framework, a new class of independent identically distributed stochastic image priors is considered to obtain a simple and tractable solution in a closed analytical form. The proposed prior model is considered in the form of a 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
Keywords Image processingMaximum likehood estimationProbabilityStochastic processesMaximum likelihood estimationWavelet transforms
Identifiers
Note Also publ. in: 5th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service, 2001, TELSIKS 2001 Date of Conference: 2001 Vol. 2 P. 583-586
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Research groups Computer Vision and Multimedia Laboratory
Stochastic Information Processing Group
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SYNYAVSKYY, A., VOLOSHYNOVSKYY, Svyatoslav, PRUDYUS, I. Wavelet-Based MAP Image Denoising Using Provably Better Class of Stochastic I.I.D. Image Models. In: Facta Universitatis - Series: Electronics and Energetics, 2001, vol. 14. https://archive-ouverte.unige.ch/unige:47505

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Deposited on : 2015-03-03

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