Advertisement

A Noise Cancelling Technique Using Non-uniform Filter Banks and Its Implementation on FPGA

  • Sang-Wook Sohn
  • Hun Choi
  • Al-Chan Yun
  • Jae-Won Suh
  • Hyeon-Deok Bae
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)

Abstract

The problem of estimating a signal that is corrupted by additive noise has been a interesting theme of digital signal processing field for several decades. Due to advantages over the linear methods, nonlinear methods based on wavelet transform have become increasingly popular. It has been shown that wavelet-thresholding algorithm generates near-optimal properties in the minimax sense. However, the wavelet-thresholding algorithm is very complex and difficult to implement it on hardware such as Field Programmable Gate Array(FPGA). Therefore, we need alternative simple approach for noise cancelling. In this paper, we propose a new noise cancelling algorithm with the binary tree structured filter banks and implement it on FPGA. To cancel the noise, we use the signal power ratio of each subband. For simple implementation, the filter banks are designed by Hadamard transform coefficients. From the results of simulations and hardware implementation, we show that the proposed algorithm produces a good results.

Keywords

Field Programmable Gate Array Hardware Implementation Wavelet Method Noise Cancelling Field Programmable Gate Array Implementation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Donoho, D.L.: Denoising by soft-Thresholding. IEEE Trans. Information Theory, 613–627 (1995)Google Scholar
  2. 2.
    Vaidyanathan, P.P.: Multirate System and Filter Banks. Prentice-Hall, Englewood Cliffs (1993)Google Scholar
  3. 3.
    Lu, W.S.: Wavelet Approaches to still Image Denosing. In: Proceedings of 31st Asiloman Conference, pp. 1705–1709 (1997)Google Scholar
  4. 4.
    Pan, Q., Zhang, L., Guanzhong, D., Hongai, Z.: Two Denoising Method by wavelet Transform. IEEE Trans. Signal Processing 47(12), 3401–3406 (1999)MATHCrossRefGoogle Scholar
  5. 5.
    Shenggian, W., et al.: Load Characteristic based Wavelet Shrinkage Denoising Algorithm. IEEE Electronics Lett. 38(9), 411–412 (2002)CrossRefGoogle Scholar
  6. 6.
    Tanaka, T., Duval, L.: Noise Reduction of Image with Multiple Subband Transforms. In: Proceedings of ICIP 2004, pp. 1209–1212 (2004)Google Scholar
  7. 7.
    Strang, G.: Wavelets and Filter Banks. Wellesley-Cambridge Press (1997)Google Scholar
  8. 8.
    Nishikawa, K., Kiya, H.: New Structure of Affine Projections Algorithm using a novel subband adaptive system. In: Proceedings of 3rd IEEE Signal Processing Workshop, pp. 364–367 (2001)Google Scholar
  9. 9.
    Mallat, S.: Wavelet tour of signal processing. Academic Press, London (1999)MATHGoogle Scholar
  10. 10.
    Donoho, D.L., Johnstone, I.M.: Threshold selection for wavelet shrinkage of noisy data. In: IEEE Proc. New Opportunities for Biomedical Engineers, pp. A24–A25 (1994)Google Scholar
  11. 11.
    Altera.: Stratix Device Handbook ver.3.3.(2005)Google Scholar
  12. 12.
    Sardy, S.: Minimax threshold for denoising complex signals with Waveshrink. IEEE Trans. Signal Processing. 48(4), 1023–1028 (2000)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sang-Wook Sohn
    • 1
  • Hun Choi
    • 2
  • Al-Chan Yun
    • 1
  • Jae-Won Suh
    • 2
  • Hyeon-Deok Bae
    • 1
  1. 1.Dep. of Electrical EngineeringChungbuk National UniversityKorea
  2. 2.Dep. of Electronic EngineeringChungbuk National UniversityKorea

Personalised recommendations