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International Journal of Computer Vision

, Volume 68, Issue 1, pp 65–82 | Cite as

Fast Anisotropic Smoothing of Multi-Valued Images using Curvature-Preserving PDE's

  • David TschumperlÉ
Regular Papers

Abstract

We are interested in PDE's (Partial Differential Equations) in order to smooth multi-valued images in an anisotropic manner. Starting from a review of existing anisotropic regularization schemes based on diffusion PDE's, we point out the pros and cons of the different equations proposed in the literature. Then, we introduce a new tensor-driven PDE, regularizing images while taking the curvatures of specific integral curves into account. We show that this constraint is particularly well suited for the preservation of thin structures in an image restoration process. A direct link is made between our proposed equation and a continuous formulation of the LIC's (Line Integral Convolutions by Cabral and Leedom (1993). It leads to the design of a very fast and stable algorithm that implements our regularization method, by successive integrations of pixel values along curved integral lines. Besides, the scheme numerically performs with a sub-pixel accuracy and preserves then thin image structures better than classical finite-differences discretizations. Finally, we illustrate the efficiency of our generic curvature-preserving approach – in terms of speed and visual quality – with different comparisons and various applications requiring image smoothing : color images denoising, inpainting and image resizing by nonlinear interpolation.

Keywords

multi-valued images data regularization anisotropic smoothing diffusion PDE's tensor-valued geometry denoising inpainting nonlinear interpolation 

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Copyright information

© Springer Science + Business Media, LLC. 2006

Authors and Affiliations

  • David TschumperlÉ
    • 1
  1. 1.Equipe Image/GREYC (UMR CNRS 6072)Caen CedexFrance

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