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Proceedings Paper

Markov random field modeling in median pyramidal transform domain for denoising applications
Author(s): Ilya Gluhovsky; Vladimir P. Melnik; Ilya Shmulevich
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Paper Abstract

We consider a median pyramidal transform for denoising applications. Traditional techniques of pyramidal denoising are similar to those in wavelet-based methods. In order to remove noise, they use the thresholding of transform coefficients. We propose to model the structure of the transform coefficients as a Markov random field. The goal of modeling transform coefficients is to retain significant coefficients on each scale and to discard the rest. Estimation of the transform coefficient structure is obtained via a Markov chain sampler. The advantage of our method is that we are able to utilize the interactions between transform coefficients, both within each scale and among the scales, which leads to denoising improvement as demonstrated by numerical simulations.

Paper Details

Date Published: 8 May 2001
PDF: 8 pages
Proc. SPIE 4304, Nonlinear Image Processing and Pattern Analysis XII, (8 May 2001); doi: 10.1117/12.424984
Show Author Affiliations
Ilya Gluhovsky, Sun Microsystems Labs. (United States)
Vladimir P. Melnik, Tampere Univ. of Technology (Finland)
Ilya Shmulevich, Tampere Univ. of Technology (Finland)

Published in SPIE Proceedings Vol. 4304:
Nonlinear Image Processing and Pattern Analysis XII
Edward R. Dougherty; Jaakko T. Astola, Editor(s)

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