PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

MDL denoising revisited
Teemu Roos, Petri Myllymäki and Jorma Rissanen
IEEE Transactions on Signal Processing Volume 57, Number 9, pp. 3347-3360, 2009.


We refine and extend an earlier MDL denoising criterion for wavelet-based denoising. We start by showing that the denoising problem can be reformulated as a clustering problem, where the goal is to obtain separate clusters for informative and non-informative wavelet coefficients, respectively. This suggests two refinements, adding a code-length for the model index, and extending the model in order to account for subband-dependent coefficient distributions. A third refinement is derivation of soft thresholding inspired by predictive universal coding with weighted mixtures. We propose a practical method incorporating all three refinements, which is shown to achieve good performance and robustness in denoising both artificial and natural signals.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:2271
Deposited By:Teemu Roos
Deposited On:13 October 2006