MDL Denoising Revisited

Details MDL Denoising Revisited MDL Denoising Revisited

2006-09-25

arxiv.org/abs/cs/0609138v1

Computer Science

Information Theory

Submitted to IEEE Transactions on Information Theory, June 2006

Scientific paper

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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