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Denoising by Inpainting

  • Robin Dirk Adam
  • Pascal PeterEmail author
  • Joachim Weickert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10302)

Abstract

The filling-in effect of diffusion processes has been successfully used in many image analysis applications. Examples include image reconstructions in inpainting-based compression or dense optic flow computations. As an interesting side effect of diffusion-based inpainting, the interpolated data are smooth, even if the known image data are noisy: Inpainting averages information from noisy sources. Since this effect has not been investigated for denoising purposes so far, we propose a general framework for denoising by inpainting. It averages multiple inpainting results from different selections of known data. We evaluate two concrete implementations of this framework: The first one specifies known data on a shifted regular grid, while the second one employs probabilistic densification to optimise the known pixel locations w.r.t. the inpainting quality. For homogeneous diffusion inpainting, we demonstrate that our regular grid method approximates the quality of its corresponding diffusion filter. The densification algorithm with homogeneous diffusion inpainting, however, shows edge-preserving behaviour. It resembles space-variant diffusion and offers better reconstructions than homogeneous diffusion filters.

Keywords

Diffusion Denoising Inpainting Densification 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Robin Dirk Adam
    • 1
  • Pascal Peter
    • 1
    Email author
  • Joachim Weickert
    • 1
  1. 1.Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Campus E1.7Saarland UniversitySaarbrückenGermany

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