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.
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Gluhovsky, I., Melnik, V. & Shmulevich, I. Markov Random Field Modeling in Median Pyramidal Transform Domain for Denoising Applications. Journal of Mathematical Imaging and Vision 16, 237–249 (2002). https://doi.org/10.1023/A:1020381711050
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DOI: https://doi.org/10.1023/A:1020381711050