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Image Denoising Using Neighbouring Contourlet Coefficients

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Advances in Neural Networks - ISNN 2008 (ISNN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5264))

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Abstract

The denoising of a natural image corrupted by Gaussian white noise is a classical problem in image processing. In this paper, a new image denoising method is proposed by using the contourlet transform. The thresholding process employs a small neighbourhood for the current contourlet coefficient to be thresholded. This is because the contourlet coefficients are correlated, and large contourlet coefficients will normally have large coefficients at its neighbour locations. Experiments show that the proposed method is better than the standard contourlet denoising and the wavelet denoising.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Concordia University, Montreal, Quebec, Canada, H3G 1M8

    Guangyi Chen & Wei-Ping Zhu

Authors
  1. Guangyi Chen
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  2. Wei-Ping Zhu
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghu University, 100084, Beijing, China

    Fuchun Sun

  2. Institute TAMS (Technical Aspects of Multimodal Systems), department of Informatics, University of Hamburg, Vogt-Koelln-Straße 30, 22527, Hamburg, Germany

    Jianwei Zhang

  3. Intel China Research Center, 8/F, Peking University, Department of Machine Intelligence, 100871, Beijing, China

    Ying Tan

  4. Department of Mathematics, Southeast University, 210096, Nanjing, China

    Jinde Cao

  5. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

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© 2008 Springer-Verlag Berlin Heidelberg

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Chen, G., Zhu, WP. (2008). Image Denoising Using Neighbouring Contourlet Coefficients. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87734-9_44

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  • DOI: https://doi.org/10.1007/978-3-540-87734-9_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87733-2

  • Online ISBN: 978-3-540-87734-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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