KSME International Journal

, Volume 18, Issue 10, pp 1738–1746 | Cite as

Enhancing nearfield acoustic holography using wavelet transform

  • Byeongsik Ko


When there are low signal to noise relationships or low coherences between measured pressure and a reference sensor, a pressure field measured and estimated by NAH (Nearfield Acoustic Holography) becomes noisy on the hologram and source planes. This paper proposes a method to obtain the high coherent de-noised pressure signals from low coherent noisy ones by combining a wavelet algorithm with NAH. The proposed method obtains the de-noised field from acoustic fields on a noise source plane reconstructed through backward propagation of NAH. Thus this method does not need high coherent pressure signals on the hologram surface while the conventional nearfield acoustic holography requires high-coherent signals. The proposed method was verified by numerical simulation using noisy signals, composed of original signals and imposed noises distributed on the hologram surface.

Key Words

Wavelet Holography Denoising Acoustics 


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  1. Borgiotti, G., Sarkissian, A., Williams, E. G. and Schuetz, L., 1990, “Conformai Generalized Near-Field Acoustic Holography for Axisymmetric Geometries,”J. Acoust. Soc. Am. Vol. 88, pp. 199–209CrossRefGoogle Scholar
  2. Chambolle, A., DeVore, R. A., Lee, N. Y. and Lucier, B. J., 1998, “Nonlinear Wavelet Image Processing: Variational Problems, Compression and Noise Removal through Wavelet Shrinkage,”IEEE Trans. Image Proc., Vol. 7, pp. 319–335MATHCrossRefMathSciNetGoogle Scholar
  3. Chang, S. G. and Vetterli, M., 1997, “Spatial Adaptive Wavelet Thresholding for Image Denoising,”Proc of IEEE Intl Conf on Image Processing, pp. 374-377Google Scholar
  4. Coifman, R. R. and Donoho, D. L., 1995,Ideal Translation Invariant De-noising, Springer VerlagGoogle Scholar
  5. Donoho, D. L., 1992, “Wavelet Thresholding and W. V. D.: A 10-minute Tour,”Intl. Conf. on Wavelets and Applications, Toulouse, France, JuneGoogle Scholar
  6. Donoho, D. L. and Johnstone, I. M., 1993, “Adaptating to Unknown Smoothness by Wavelet Shrinkage,”Technical Report, Department of Statistics, Stanford UniversityGoogle Scholar
  7. Donoho, D. L. and Johnstone, I. M., 1994, “Ideal Spatial Adaptation Via Wavelet Thresholding,”Biometrika, Vol. 81, pp. 425–455MATHCrossRefMathSciNetGoogle Scholar
  8. Donoho, D. L., 1995, “De-Noising by Soft- Threshholding,”IEEE Trans. Info. Theory, Vol. 41, No. 3, pp. 613–627MATHCrossRefMathSciNetGoogle Scholar
  9. Dumbacher, S. M., Brown, D. L., Blough, J. R. and Bono, R. W., 2000, “Practical Aspects of Making NAH Measurements,”SAE Transaction, Vol. 108, No. 6, Part 2., pp. 3081–3090Google Scholar
  10. Goodman, W., 1968,Introduction to Fourier Optics, McGraw-HillGoogle Scholar
  11. Haar, A., 1910, “Zur Theorie Der Orthogonalen Funktionen-Systeme,”Math. Ann., Vol. 69, pp. 331–371MATHCrossRefMathSciNetGoogle Scholar
  12. Maynard, J. D., Williams, E. G. and Lee Y., 1985, “Nearfield Acoustic Holography: I. Theory of Generalized Holography and the Development of NAH,”J. Acoust. Soc. Am. Vol. 78, No. 4, pp. 1395–1413CrossRefGoogle Scholar
  13. Meyer, Y., 1993,Wavelets: Algorithms and Applications, SIAMGoogle Scholar
  14. Morlet, J., 1983, “Sampling Theory and Wave Propagation,” in NATO ASI Series, Vol. 1,Issues in Acoustic signal/Image processing and recognition, C. H. Chen, ed. Springer-Verlag, Berlin, pp. 233–261Google Scholar
  15. Said, A. and Pearlman, W. A., 1996, “A New Fast and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees,”IEEE Trans. Circ. and Syst. Video Tech., Vol. 6, No. 3, pp. 243–250CrossRefGoogle Scholar
  16. Shao, X. and Cherkassky, V., 1998, “Model Selection for Wavelet-Based Signal Estimation,”Proc. IEEE Intl. Joint Conf. on Neural Networks, Vol.2, pp. 843–848Google Scholar
  17. Shapiro, J. M., 1993, “Embedded Image Coding Using Zerotrees of Wavelet Coefficients,”IEEE Trans. Signal Processing, Vol.41, pp. 3445–3462, Dec.MATHCrossRefGoogle Scholar
  18. Simoncelli, E. P., 1999, “Bayesian De-noising of Visual Images in the Wavelet Domain,” inBayesian Inference in Wavelet Based Models, Muller and Vidakovic (eds.), Springer VerlagGoogle Scholar
  19. Verosi, W. D. and Maynard, J. D., 1987, “Nearfield Acoustic Holography: II. Holographic Reconstruction Algorithms and Computer Algorithms,”J. Acoust. Soc. Am. Vol. 81, No. 5, pp. 1307–1322CrossRefGoogle Scholar
  20. Wang, Z. and Wu, S. F., 1997, “Helmholtz Equation-Least-Squares Method for Reconstructing the Acoustic Pressure Field,”J. Acoust. Soc. Am., Vol. 102, No. 4, pp. 2020–2032CrossRefGoogle Scholar

Copyright information

© The Korean Society of Mechanical Engineers (KSME) 2004

Authors and Affiliations

  1. 1.Korea simulation technology Inc.SeoulKorea

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