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A Study on Wavelet-based Denoising Algorithm for Signal Reconstruction in Mixed Noise Environments  

Bae, Sang-Bum (School of Electrical and Control Engineering, Pukyong National University)
Kim, Nam-Ho (School of Electrical and Control Engineering, Pukyong National University)
Publication Information
Journal of information and communication convergence engineering / v.5, no.1, 2007 , pp. 1-6 More about this Journal
Abstract
In the process of the acquisition, storage, transmission of signals, noises are generated by various causes and the degradation phenomenon by noises tends to generate serious errors for the signal with information. So, in order to analyze and remove these noises, studies on numerous mathematical methods such as the Fourier transform have been implemented. And recently there have been many ongoing wavelet-based denoising algorithms representing excellent characteristics in time-frequency localization and multiresolution analysis, but the method to remove additive white Gaussian noise (AWGN) and the impulse noise simultaneously was not given. So, to reconstruct the corrupted signal by noises, in this paper a novel wavelet-based denoising algorithm was proposed and using signal-to-noise ratio (SNR) this method was compared to conventional methods.
Keywords
AWGN; denoising; impulse noise; multiresolution; time-frequency localization; wavelet;
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  • Reference
1 D. L. Donoho, 'De-noising by soft-thresholding', IEEE Trans. Inf. Theory, vol. 41, no. 3, pp. 613-627, May 1995   DOI   ScienceOn
2 M. Lang, H. guo, J. E. Odegard, C. S. Burrus, and R. O. Wells, 'Noise reduction using and undecimated discrete wavelet transform', IEEE Signal Processing Lett., vol. 3, no. 1, pp. 10-12. Jan. 1996   DOI   ScienceOn
3 H. K. Kwan, 'Tunable and variable passive digital filters for multimedia signal processing,' in Proc. IEEE Int. Video and Speech Processing, pp. 229-232, May 2001
4 P. L. Ainsleigh and C. K. Chui, 'A B-wavelet- based noise-reduction algorithm', IEEE Trans. Signal Processing, vol. 44, pp. 1279-1284, May 1996   DOI   ScienceOn
5 S. G. Mallat, 'A theory for multiresolution signal decomposition : The wavelet representation', IEEE Trans. on Pattern Recognition and Machine intelligence, vol. 11, no. 7, pp. 674-693, July 1989   DOI   ScienceOn
6 A. Grossmann and J. Morlet, 'Decomposition of Hardy functions into square integrable wavelets of constant shape,' SIAM J. Math., vol. 15, pp. 723-736, 1984   DOI
7 I. Daubechies, 'Orthonormal basis of compactly supported wavelets,' Comm. Pure Applied Math., vol. 41, no. 7, pp. 909-996, 1988   DOI
8 L. Yin and Y. Neuvo, 'Adaptive FIR-WOS Filtering,' Proc. of the IEEE Int. Symp. On Circuits and Systems, pp. 2637-2640, May 1992
9 I. Daubechies, 'The wavelet transform, time-frequency localization and signal analysis,' IEEE Trans. Inform. Theory, vol. 36, no. 5, pp. 961-1005, Sept. 1990   DOI   ScienceOn
10 Y, H. Lee and S. Tantaratana, 'Decision-based order statistic filters,' IEEE Trans. Signal Processing, vol. 38, pp. 406-420, March 1990   DOI   ScienceOn
11 A. Wroblewski, T. Erl, and J. A. Nossek, 'Bireciprocal lattice wave digital filters with almost linear phase response,' in Proc. IEEE Int. Speech and Signal Processing, vol. 2, pp. 805-808, April 2003
12 C. K. Chui and J. Z. Wang, 'On compactly supported spline wavlets and a duality principle,' Trans, Amer. Math. Soc., vol. 330, no. 2, pp. 903-915, April. 1992   DOI   ScienceOn
13 Q. Pan, L. Zhang, G. Dai, and H. Zhang, 'Two denoising methods by wavelet transform', IEEE Trans. Signal Processing, vol. 47, pp. 3401-3406, Dec. 1999   DOI   ScienceOn
14 D. L. Donoho and I. M. Johnstone, 'Adapting to unknown smoothness via wavelet shrinkage,' J. Amer. Statist. Assoc., vol. 90, no. 432, pp. 1200-1224, 1995   DOI
15 T. Chen and H. R. Wu, 'Adaptive Impulse Detection Using Center-Weighted Median Filters,' IEEE Trans. Signal Processing Lett., vol. 8, pp. 1-3, Jan. 2001   DOI   ScienceOn
16 M. C. E. Rosas-Orea, M. Hernandez-Diaz, V. Alarcon-Aquino, and L. G. Guerrero-Ojeda, 'A Comparative Simulation Study of Wavelet Based Denoising Algorithms,' in Proc. 15th, Int. Conf. Electronics, Communications and Computers, pp. 125-130, 2005