Abstract
A new signal-denoising approach based on DT-CWT (Dual-Tree Complex Wavelet Transform) is presented in this paper to extract feature information from microstructure profile. It takes advantage of shift invariance of DT-CWT, non-Gaussian probability distribution for the wavelet coefficients and the statistical dependencies between a coefficient and its parent. This approach substantially improved the performance of classical wavelet denoising algorithms, both in terms of SNR and in terms of visual artifacts. A simulated MEMS microstructure signal is analyzed.
| Original language | English |
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| Pages (from-to) | 69-72 |
| Number of pages | 4 |
| Journal | Key Engineering Materials |
| Volume | 381-382 |
| Publication status | Published - 17 Jul 2008 |