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Discrete Wavelet Diffusion for Image Denoising

  • Kashif Rajpoot
  • Nasir Rajpoot
  • J. Alison Noble
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5099)

Abstract

Nonlinear diffusion, proposed by Perona-Malik, is a well-known method for image denoising with edge preserving characteristics. Recently, nonlinear diffusion has been shown to be equivalent to iterative wavelet shrinkage, but only for (1) Mallat-Zhong dyadic wavelet transform and (2) Haar wavelet transform. In this paper, we generalize the equivalence of nonlinear diffusion to non-linear shrinkage in the standard discrete wavelet transform (DWT) domain. Two of the major advantages of the standard DWT are its simplicity (as compared to 1) and its potential to benefit from a greater range of orthogonal and biorthogonal filters (as compared to both 1 and 2). We also extend the wavelet diffusion implementation to multiscale. The qualitative and quantitative results shown for a variety of images contaminated with noise demonstrate the promise of the proposed standard wavelet diffusion.

Keywords

Discrete Wavelet Transform Discrete Wavelet Decomposition Level Nonlinear Diffusion Haar Wavelet 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Kashif Rajpoot
    • 1
  • Nasir Rajpoot
    • 2
  • J. Alison Noble
    • 1
  1. 1.Department of Engineering ScienceUniversity of OxfordUK
  2. 2.Department of Computer ScienceUniversity of WarwickUK

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