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Diffusion-Inspired Shrinkage Functions and Stability Results for Wavelet Denoising

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Abstract

We study the connections between discrete one-dimensional schemes for nonlinear diffusion and shift-invariant Haar wavelet shrinkage. We show that one step of a (stabilised) explicit discretisation of nonlinear diffusion can be expressed in terms of wavelet shrinkage on a single spatial level. This equivalence allows a fruitful exchange of ideas between the two fields. In this paper we derive new wavelet shrinkage functions from existing diffusivity functions, and identify some previously used shrinkage functions as corresponding to well known diffusivities. We demonstrate experimentally that some of the diffusion-inspired shrinkage functions are among the best for translation-invariant multiscale wavelet denoising. Moreover, by transferring stability notions from diffusion filtering to wavelet shrinkage, we derive conditions on the shrinkage function that ensure that shift invariant single-level Haar wavelet shrinkage is maximum–minimum stable, monotonicity preserving, and variation diminishing.

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Authors and Affiliations

  1. Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Saarland University, Building 27, 66041, Saarbrücken, Germany

    Pavel Mrázek & Joachim Weickert

  2. Faculty of Mathematics and Computer Science, University of Mannheim, D7, 27, 68131, Mannheim, Germany

    Gabriele Steidl

Authors
  1. Pavel Mrázek
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  2. Joachim Weickert
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  3. Gabriele Steidl
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Correspondence to Pavel Mrázek.

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First online version published in June, 2005

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Mrázek, P., Weickert, J. & Steidl, G. Diffusion-Inspired Shrinkage Functions and Stability Results for Wavelet Denoising. Int J Comput Vision 64, 171–186 (2005). https://doi.org/10.1007/s11263-005-1842-y

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  • DOI: https://doi.org/10.1007/s11263-005-1842-y

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