• DocumentCode
    2648617
  • Title

    Study to the image denoising algorithm based on multiwavelet transforms

  • Author

    Zhang, Xiao-wei ; Zhu, Lei ; Zheng, Xiong-bo

  • Author_Institution
    Harbin Eng. Univ., Harbin
  • Volume
    4
  • fYear
    2007
  • fDate
    2-4 Nov. 2007
  • Firstpage
    1803
  • Lastpage
    1807
  • Abstract
    Compared to the scalar wavelet, the multiwavelet has the properties of orthogonality, short support, real symmetry, high order vanishing moment and so on, however, it does not work well in image denoising techniques, primarily due to not making full use of the characteristic property of images in the multiwavelet domain. By transforming the noisy images to the multiwavelet domain, and applying a special difference scheme of the Laplacian operator and also considering of the image´s fractal dimension of high frequency subband in the multiwavelet domain, the paper proposes an adaptive multiwavelets thresholding algorithm - AMT algorithm, which can automatically determine the wavelet shrinkage thresholding in the multiwavelet domain without a priori knowledge of image, for instance, the variance of image noise. The result of the simulation experiment indicates, that the effect of the AMT algorithm is perfectly well, especially for high degraded images.
  • Keywords
    Laplace transforms; fractals; image denoising; wavelet transforms; Laplacian operator; adaptive multiwavelet thresholding algorithm; image denoising algorithm; multiwavelet transform; Image denoising; Notice of Violation; Pattern analysis; Pattern recognition; Wavelet analysis; Laplacian operator; fractal dimension of image; image denosing; multiwavelet transforms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4244-1065-1
  • Electronic_ISBN
    978-1-4244-1066-8
  • Type

    conf

  • DOI
    10.1109/ICWAPR.2007.4421746
  • Filename
    4421746