Mask-Specific Inpainting with Deep Neural Networks

  • Rolf Köhler
  • Christian Schuler
  • Bernhard Schölkopf
  • Stefan Harmeling
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)

Abstract

Most inpainting approaches require a good image model to infer the unknown pixels. In this work, we directly learn a mapping from image patches, corrupted by missing pixels, onto complete image patches. This mapping is represented as a deep neural network that is automatically trained on a large image data set. In particular, we are interested in the question whether it is helpful to exploit the shape information of the missing regions, i.e. the masks, which is something commonly ignored by other approaches. In comprehensive experiments on various images, we demonstrate that our learning-based approach is able to use this extra information and can achieve state-of-the-art inpainting results. Furthermore, we show that training with such extra information is useful for blind inpainting, where the exact shape of the missing region might be uncertain, for instance due to aliasing effects.

Keywords

Inpainting Deep learning Neural-nets Multi-layer-perceptrons 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Rolf Köhler
    • 1
  • Christian Schuler
    • 1
  • Bernhard Schölkopf
    • 1
  • Stefan Harmeling
    • 1
  1. 1.Max Planck Institute for Intelligent SystemsTübingenGermany

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