An adaptive Bayesian wavelet thresholding approach to Multifractal signal denoising

2004 ◽  
Author(s):  
Abd-Krim Seghouane
Keyword(s):  
Signal Denoising ◽  
Wavelet Thresholding ◽  
Bayesian Wavelet

A Denoising Method Based on EEMD and Interval-Thresholding Strategy

2014 ◽  
Vol 902 ◽  
pp. 336-340 ◽  
Author(s):  
Zhi Zhou ◽  
Xing Man Yang ◽  
Gang Chen
Keyword(s):  
Simulation Experiment ◽  
Adaptive Signal Processing ◽  
Basis Functions ◽  
Signal Denoising ◽  
Wavelet Thresholding ◽  
Denoising Method ◽  
Signal Processing Method ◽  
Adaptive Signal ◽  
Conventional Signal ◽  
Selection Of

As a conventional signal denoising method, wavelet thresholding denoising has problems including selection of basis vectors and poor denoising effect. EMD is an expansion of basis functions that are signal-dependent, but with the problem of mode mixing. In order to solve these problems, a denoising method based on EEMD and interval-thresholding strategy, an adaptive signal processing method is proposed, which can achieve good effects for signal denoising. Firstly, investigated signal is decomposed into IMFs by EEMD adaptively. Then, each IMF is denoising by interval-thresholding method based on sparse code shrinkage. Lastly, the denoised signal is reconstructed by denoised IMFs. Moreover, the presented method is validated by numerical simulation experiment.

2-Wire 4-Duplex Transmission Signal Denoising Using Nonlinear Wavelet Thresholding

2006 ◽  
Author(s):  
I Nyoman Sukadana ◽  
Shogo Nakamura ◽  
Hiroto Saito ◽  
Minoru Horie ◽  
Yukichi Horioka ◽  
...  
Keyword(s):  
Signal Denoising ◽  
Wavelet Thresholding

IMAGE DENOISING BASED ON WAVELET SHRINKAGE USING NEIGHBOR AND LEVEL DEPENDENCY

2009 ◽  
Vol 07 (03) ◽  
pp. 299-311 ◽  
Author(s):  
DONGWOOK CHO ◽  
TIEN D. BUI ◽  
GUANGYI CHEN
Keyword(s):  
Signal To Noise Ratio ◽  
Signal Denoising ◽  
Wavelet Thresholding ◽  
Wavelet Coefficients ◽  
Wavelet Shrinkage ◽  
Minimum Risk ◽  
Shrinkage Methods ◽  
Risk Estimator ◽  
Visual Artifacts ◽  
Unbiased Risk

Since Donoho et al. proposed the wavelet thresholding method for signal denoising, many different denoising approaches have been suggested. In this paper, we present three different wavelet shrinkage methods, namely NeighShrink, NeighSure and NeighLevel. NeighShrink thresholds the wavelet coefficients based on Donoho's universal threshold and the sum of the squares of all the wavelet coefficients within a neighborhood window. NeighSure adopts Stein's unbiased risk estimator (SURE) instead of the universal threshold of NeighShrink so as to obtain the optimal threshold with minimum risk for each subband. NeighLevel uses parent coefficients in a coarser level as well as neighbors in the same subband. We also apply a multiplying factor for the optimal universal threshold in order to get better denoising results. We found that the value of the constant is about the same for different kinds and sizes of images. Experimental results show that our methods give comparatively higher peak signal to noise ratio (PSNR), are much more efficient and have less visual artifacts compared to other methods.

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