MDL Denoising Revisited
<|reference_start|>MDL Denoising Revisited: 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.<|reference_end|>
@article{roos2006mdl,
    title={MDL Denoising Revisited},
    author={Teemu Roos, Petri Myllym"aki, Jorma Rissanen},
    journal={arXiv preprint arXiv:cs/0609138},
    year={2006},
    doi={10.1109/TSP.2009.2021633},
    archivePrefix={arXiv},
    eprint={cs/0609138},
    primaryClass={cs.IT math.IT}
}