Name
Abstract | ||
|---|---|---|
In the present study, a novel signal restoration method from noisy data samples is presented and is termed as "signal split (SSplit)" approach. The new method utilizes Stein unbiased risk estimate estimator to split the signal, the Lipschitz exponents to identify noise elements and a heuristic approach for the signal reconstruction. However, unlike many noise removal techniques, the present method works only in the non-orthogonal domain. Signal restoration was performed on each individual part by finding the best compromise between the data samples and the smoothing criteria. Statistical results are quite promising and suggest better performance than the conventional shrinkage. Furthermore, the proposed method preserves the energy of the sharp peaks and edges which, is not however, the case for classical shrinkage methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1186/1687-6180-2012-38 | EURASIP J. Adv. Sig. Proc. |
Keywords | Field | DocType |
continuous wavelet transform, wavelet transform modulus maxima, split or segmentation, Stein unbiased risk estimate, thresholding, modulus maxima, Lipschitz exponent | Shrinkage,Computer science,Continuous wavelet transform,Artificial intelligence,Lipschitz continuity,Thresholding,Computer vision,Heuristic,Mathematical optimization,Algorithm,Smoothing,Signal reconstruction,Estimator | Journal |
Volume | Issue | ISSN |
2012 | 1 | 1687-6180 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jalil Bushra | 1 | 7 | 4.84 |
| Fauvet Eric | 2 | 7 | 6.12 |
| Olivier Laligant | 3 | 38 | 10.66 |