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Properties of Higher Order Nonlinear Diffusion Filtering

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Abstract

This paper provides a mathematical analysis of higher order variational methods and nonlinear diffusion filtering for image denoising. Besides the average grey value, it is shown that higher order diffusion filters preserve higher moments of the initial data. While a maximum-minimum principle in general does not hold for higher order filters, we derive stability in the 2-norm in the continuous and discrete setting. Considering the filters in terms of forward and backward diffusion, one can explain how not only the preservation, but also the enhancement of certain features in the given data is possible. Numerical results show the improved denoising capabilities of higher order filtering compared to the classical methods.

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Authors and Affiliations

  1. Fraunhofer-Institut für Techno- und Wirtschaftsmathematik, Abteilung Bildverarbeitung, Fraunhofer-Platz 1, 67663, Kaiserslautern, Germany

    S. Didas

  2. Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Saarland University, Campus E 1.1, 66041, Saarbrücken, Germany

    J. Weickert & B. Burgeth

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  1. S. Didas
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  2. J. Weickert
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Correspondence to S. Didas.

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Didas, S., Weickert, J. & Burgeth, B. Properties of Higher Order Nonlinear Diffusion Filtering. J Math Imaging Vis 35, 208–226 (2009). https://doi.org/10.1007/s10851-009-0166-x

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