Xue-feng ZHANG(张雪峰), Chun-xia KANG(康春霞), Feng PEI(裴峰), Zhi-jie ZHANG(张志杰)
(Key Laboratory of Instrumentation Science & Dynamic Measurement(Ministry of Education),North University of China, Taiyuan 030051, China)
Abstract-By utilizing the capability of high-speed computing, powerful real-time processing of TMS320F2812 DSP, wavelet thresholding denoising algorithm is realized based on Digital Signal Processors. Based on the multi-resolution analysis of wavelet transformation, this paper proposes a new thresholding function, to some extent, to overcome the shortcomings of discontinuity in hard-thresholding function and bias in soft-thresholding function. The threshold value can be abtained adaptively according to the characteristics of wavelet coefficients of each layer by adopting adaptive threshold algorithm and then the noise is removed. The simulation results show that the improved thresholding function and the adaptive threshold algorithm have a good effect on denoising and meet the criteria of smoothness and similarity between the original signal and denoising signal.
Key words-Mallat algorithm; wavelet denoising; thresholding function; adaptive threshold; Digital Signal Processors
Manuscript Number: 1674-8042(2011)03-0272-04
doi: 10.3969/j.issn.1674-8042.2011.03.017
References
[1] O. Rioul, M. Vetterli, 1991. Wavelets and signal processing. IEEE Sig. Proc. Mag, 8(4): 14-38.
[2] T. R. Downie, B. W. Silverman, 1998. The discrete multiple wavelet transform and thresholding methods. IEEE Trans on Signal Processing, 46(9): 2558-2561.
[3] Yong-sheng Dong, Xu-ming Yi, 2005. Wavelet denoising based on four modified function for threshold estimation. Journal of Mathematics, 14(7): 710-732.
[4] S. Mallat, 1989. A theory of multi-resolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal Machine Intell, 11: 674-693.
[5] S. G. Mallat, 1989. A theory for multi-resolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Reco. and Machine Int., 11(7): 674-693.
[6] J. S. Geronimo, D. P. Hardin, P. R. Massoput. Fractal function and wavelet expansions based on several scaling functions. J. Approx. Theory, 41(3): 613-627.
[7] D. L. Donoho, 1995. De-noising by soft-thresholding[J]. IEEE Trans. Inform. Theory, 41(3): 613-627.
[8] M. Jansen, 2001. Noise Reduction by Wavelet Thresholding. Springer-Verlag, New York, p.160-165.
[9] IEEE92, 1992. Special issue on wavelet transform and multiresolution signal analysis. IEEE Trans. Info. Theory, 38(2, Part Ⅱ): 529-924.
[10] Quan Pan, Pan Zhang, Guanzhong Dai, 1999. Two de-noising methods by wavelet transform. IEEE Trans. Signal Processing, 47(12): 3401-3406.
[11] Rui-zhen Zhao, Guo-xiang Song, Hong Wang, 2001. Better threshold estimation of wavelet for coefficients of improving denoising. Journal of Northwestern Polytechnical University, 19(4): 625-628.
[12] Su Li, Guoliang Zhao, 2005. Translation-invariant wavelet denoising method with improved thresholding. IEEE International Symposium, 1(12-14): 619-622.
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