Bayesian wavelet denoising: Besov priors and non-Gaussian noises

2001 ◽  
Vol 81 (1) ◽  
pp. 55-67 ◽  
Author(s):  
D. Leporini ◽  
J.-C. Pesquet
Keyword(s):  
Wavelet Denoising ◽  
Non Gaussian ◽  
Bayesian Wavelet

A Bayesian wavelet packet denoising criterion for mechanical signal with non-Gaussian characteristic

Measurement ◽  
2019 ◽  
Vol 138 ◽  
pp. 702-712 ◽  
Author(s):  
Guo-dong Yue ◽  
Xiu-shi Cui ◽  
Yuan-yuan Zou ◽  
Xiao-tian Bai ◽  
Yu-Hou Wu ◽  
...  
Keyword(s):  
Wavelet Packet ◽  
Mechanical Signal ◽  
Non Gaussian ◽  
Bayesian Wavelet

Bayesian wavelet denoising and evolutionary calibration

2004 ◽  
Vol 14 (6) ◽  
pp. 566-589 ◽  
Author(s):  
Adelino R. Ferreira da Silva
Keyword(s):  
Wavelet Denoising ◽  
Bayesian Wavelet

Image Enhancement Method for Steel Surface Defects Based on DT-CWT

2013 ◽  
Vol 380-384 ◽  
pp. 3686-3689
Author(s):  
Zhi Xin Chen ◽  
Ping Wang ◽  
Shi Kun Xie
Keyword(s):  
Surface Defects ◽  
Signal To Noise Ratio ◽  
Wavelet Denoising ◽  
Complex Wavelet Transform ◽  
Enhancement Method ◽  
Shift Invariance ◽  
Gaussian Probability Distribution ◽  
Simulation Results ◽  
Non Gaussian ◽  
Better Than

A method based on Dual-Tree Complex Wavelet Transform (DT-CWT) was proposed for enhancing the images. By taking into account the non-Gaussian probability distribution and the statistical dependencies among wavelet coefficients of some signals, and by taking advantage of near shift-invariance of DT-CWT, it can obtain higher signal-to-noise ratio (SNR) than common wavelet denoising methods. The simulation results show that the proposed method is better than the traditional methods. It has a good enhancement performance which can improve the details of the image automatically.

Signal Denoising of MEMS Microstructure Profile

2008 ◽  
Vol 381-382 ◽  
pp. 69-72
Author(s):  
Kai Hu ◽  
Xiang Qian Jiang ◽  
Xiao Jun Liu
Keyword(s):  
Wavelet Transform ◽  
Probability Distribution ◽  
Wavelet Denoising ◽  
Signal Denoising ◽  
Complex Wavelet Transform ◽  
Shift Invariance ◽  
Gaussian Probability Distribution ◽  
Feature Information ◽  
Visual Artifacts ◽  
Non Gaussian

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.

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