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Vector Wavelet Thresholding for Vector Field Denoising

Authors:

Michel A. Westenberg,

Thomas ErtlAuthors Info & Claims

VIS '04: Proceedings of the conference on Visualization '04

Page 598.25

https://doi.org/10.1109/VISUAL.2004.108

Published: 10 October 2004 Publication History

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Abstract

Noise reduction is an important preprocessing step for many visualization techniques that make use of feature extraction. We propose a method for denoising 2-D vector fields that are corrupted by additive noise. The method is based on the vector wavelet transform and wavelet coefficient thresholding. We compare our wavelet-based denoising method with Gaussian filtering, and test the effect of these methods on the signal-to-noise ratio (SNR) of the vector fields before and after denoising. We also study the effect on relevant details for visualization, such as vortex measures. The results show that for low SNR, Gaussian filtering with large kernels has a somewhat higher performance than the wavelet-based method in terms of SNR. For larger SNR, the wavelet-based method outperforms Gaussian filtering. This is mostly due to the fact that Gaussian filtering tends to remove small details, which are preserved by the wavelet-based method.

References

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{2} S. G. Chang, B. Yu, and M. Vetterli. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Processing, 9(9):1532-1546, 2000.

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{3} D. L. Donoho. De-noising by soft thresholding. IEEE Trans. Information Theory, 41:613-627, May 1995.

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{4} J. E. Fowler and L. Hua. Wavelet transforms for vector fields using omnidirectionally balanced multiwavelets. IEEE Trans. Signal Processing, 50:3018-3027, 2002.

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{6} H. H. Tan, L.-X. Shen, and J. Y. Tham. New biorthogonal multiwavelets for image compression. Signal Processing, 79(1):45-65, 1999.

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{7} X.-G. Xia and B. W. Suter. Vector-valued wavelets and vector filter banks. IEEE Trans. Signal Processing, 44(3):508-518, 1996.

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Vector Wavelet Thresholding for Vector Field Denoising
1. Computing methodologies
  1. Artificial intelligence
  2. Computer graphics
2. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis

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VIS '04: Proceedings of the conference on Visualization '04

October 2004

667 pages

ISBN:0780387880

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IEEE Computer Society

United States

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Published: 10 October 2004

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Underwater acoustic signal denoising based on sparse TQWT and wavelet thresholding

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