Evaluating a general class of filters for image denoising

Authors:
Luis Pizarro;Stephan Didas;Frank Bauer;Joachim Weickert
Affiliations:
Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Saarbrücken, Germany;Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Saarbrücken, Germany;Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Saarbrücken, Germany;Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Saarbrücken, Germany
Venue:
SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis
Year:
2007

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Abstract

Recently, an energy-based unified framework for image denoising was proposed by Mrázek et al. [10], from which existing nonlinear filters such as M-smoothers, bilateral filtering, diffusion filtering and regularisation approaches, are obtained as special cases. Such a model offers several degrees of freedom (DOF) for tuning a desired filter. In this paper, we explore the generality of this filtering framework in combining nonlocal tonal and spatial kernels. We show that Bayesian analysis provides suitable foundations for restricting the parametric space in a noisedependent way. We also point out the relations among the distinct DOF in order to guide the selection of a combined model, which itself leads to hybrid filters with better performance than the previously mentioned special cases. Moreover, we show that the existing trade-off between the parameters controlling similarity and smoothness leads to similar results under different settings.