A Variational Framework for Non-local Image Inpainting

  • Pablo Arias
  • Vicent Caselles
  • Guillermo Sapiro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5681)

Abstract

Non-local methods for image denoising and inpainting have gained considerable attention in recent years. This is in part due to their superior performance in textured images, a known weakness of purely local methods. Local methods on the other hand have demonstrated to be very appropriate for the recovering of geometric structure such as image edges. The synthesis of both types of methods is a trend in current research. Variational analysis in particular is an appropriate tool for a unified treatment of local and non-local methods. In this work we propose a general variational framework for the problem of non-local image inpainting, from which several previous inpainting schemes can be derived, in addition to leading to novel ones. We explicitly study some of these, relating them to previous work and showing results on synthetic and real images.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Pablo Arias
    • 1
  • Vicent Caselles
    • 1
  • Guillermo Sapiro
    • 2
  1. 1.Universitat Pompeu Fabra, DTICBarcelonaSpain
  2. 2.University of Minnesota, ECEMinneapolisUSA

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