Name
Abstract | ||
|---|---|---|
We refine and extend an earlier minimum description length (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 noninformative 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 the 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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TSP.2009.2021633 | IEEE Transactions on Signal Processing |
Keywords | DocType | Volume |
signal denoising,wavelet transforms,mdl denoising,clustering problem,earlier minimum description length,model index,predictive universal coding,subband-dependent coefficient distributions,wavelet-based denoising,minimum description length (mdl) principle,denoising,wavelets,minimum description length,indexing terms | Journal | 57 |
Issue | ISSN | Citations |
9 | 1053-587X | 17 |
PageRank | References | Authors |
1.24 | 23 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Teemu Roos | 1 | 436 | 61.32 |
| Petri Myllymaki | 2 | 69 | 9.84 |
| Jorma Rissanen | 3 | 1665 | 798.14 |