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Denoising of Three Dimensional Data Cube Using Bivariate Wavelet Shrinking

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Image Analysis and Recognition (ICIAR 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6111))

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Abstract

The denoising of a natural signal/image corrupted by Gaussian white noise is a classical problem in signal/image processing. However, it is still in its infancy to denoise high dimensional data. In this paper, we extended Sendur and Selesnick’s bivariate wavelet thresholding from two-dimensional image denoising to three dimensional data denoising. Our study shows that bivariate wavelet thresholding is still valid for three dimensional data. Experimental results confirm its superiority.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, Concordia University, Montreal, Quebec, Canada, H3G 1M8

    Guangyi Chen

  2. Department of Computer Science and Software Engineering, Concordia University, Montreal, Quebec, Canada, H3G 1M8

    Tien D. Bui & Adam Krzyzak

Authors
  1. Guangyi Chen
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  2. Tien D. Bui
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  3. Adam Krzyzak
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Editor information

Editors and Affiliations

  1. Faculty of Engineering, Institute of Biomedical Engineering, Rua Dr. Roberto Frias, University of Porto, 4200-465, Porto, Portugal

    Aurélio Campilho

  2. Department of Electrical and Computer Engineering, University of Waterloo, N2L 3G1, Waterloo, Ontario, Canada

    Mohamed Kamel

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© 2010 Springer-Verlag Berlin Heidelberg

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Chen, G., Bui, T.D., Krzyzak, A. (2010). Denoising of Three Dimensional Data Cube Using Bivariate Wavelet Shrinking. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2010. Lecture Notes in Computer Science, vol 6111. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13772-3_5

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  • DOI: https://doi.org/10.1007/978-3-642-13772-3_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13771-6

  • Online ISBN: 978-3-642-13772-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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