Title :
Signal denoising via median filter & wavelet shrinkage based on heavy-tailed modeling
Author :
Wei, Guang-Fen ; Jian, Tao ; Qiu, Rong-jian
Author_Institution :
Dept. of Electron. Eng., Shandong Inst. of Bus. & Technol.
Abstract :
Heavy-tailed noise has more outliers than Gaussian noise, and traditional wavelet threshold cannot suppress outliers. A new method combining wavelet threshold and median filter is proposed. After suppressing the outliers in signal through median filter, the wavelet threshold is applied and remained noise is removed further. The experimental error analysis shows that the new method can suppress heavy-tailed noise effectively, and it is more robust than classic wavelet threshold
Keywords :
Gaussian noise; median filters; signal denoising; wavelet transforms; Gaussian noise; heavy-tailed modeling; median filter; signal denoising; wavelet shrinkage; Data mining; Error analysis; Filters; Gaussian distribution; Gaussian noise; Noise reduction; Noise robustness; Signal denoising; Wavelet analysis; White noise;
Conference_Titel :
Signal Processing, 2006 8th International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9736-3
Electronic_ISBN :
0-7803-9736-3
DOI :
10.1109/ICOSP.2006.344443