Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지

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)
  • Published : 2007.03.30
Download PDF
⟨ Previous Next ⟩

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

References

  1. 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
  2. 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 https://doi.org/10.2307/2291512
  3. D. L. Donoho, 'De-noising by soft-thresholding', IEEE Trans. Inf. Theory, vol. 41, no. 3, pp. 613-627, May 1995 https://doi.org/10.1109/18.382009
  4. 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 https://doi.org/10.1109/97.475823
  5. 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 https://doi.org/10.1109/78.806084
  6. 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 https://doi.org/10.1109/78.502342
  7. 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 https://doi.org/10.2307/2153941
  8. 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 https://doi.org/10.1137/0515056
  9. 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 https://doi.org/10.1109/34.192463
  10. I. Daubechies, 'The wavelet transform, time-frequency localization and signal analysis,' IEEE Trans. Inform. Theory, vol. 36, no. 5, pp. 961-1005, Sept. 1990 https://doi.org/10.1109/18.57199
  11. I. Daubechies, 'Orthonormal basis of compactly supported wavelets,' Comm. Pure Applied Math., vol. 41, no. 7, pp. 909-996, 1988 https://doi.org/10.1002/cpa.3160410705
  12. 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
  13. 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
  14. Y, H. Lee and S. Tantaratana, 'Decision-based order statistic filters,' IEEE Trans. Signal Processing, vol. 38, pp. 406-420, March 1990 https://doi.org/10.1109/29.106860
  15. L. Yin and Y. Neuvo, 'Adaptive FIR-WOS Filtering,' Proc. of the IEEE Int. Symp. On Circuits and Systems, pp. 2637-2640, May 1992
  16. 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 https://doi.org/10.1109/97.889633