Markov Random Field Modeling in Median Pyramidal Transform Domain for Denoising Applications View Full Text


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Article Info

DATE

2002-05

AUTHORS

Ilya Gluhovsky, Vladimir Melnik, Ilya Shmulevich

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. A technique is proposed to estimate the parameters of the field's distribution. 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. More... »

PAGES

237-249

Identifiers

URI

http://scigraph.springernature.com/pub.10.1023/a:1020381711050

DOI

http://dx.doi.org/10.1023/a:1020381711050

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1034737650


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