Deep Edge-Aware Filters

  • Xu L
  • Ren J
  • Yan Q
 et al. 
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Abstract

There are many edge-aware filters varying in their construction forms and filtering properties. It seems impossible to uniformly represent and accelerate them in a single framework. We made the attempt to learn a big and important family of edge-aware operators from data. Our method is based on a deep convolutional neural network with a gradient domain training procedure, which gives rise to a powerful tool to approximate var-ious filters without knowing the original models and implementation details. The only difference among these operators in our system becomes merely the learned parameters. Our system en-ables fast approximation for complex edge-aware filters and achieves up to 200x acceleration, re-gardless of their originally very different imple-mentation. Fast speed can also be achieved when creating new effects using spatially varying filter or filter combination, bearing out the effective-ness of our deep edge-aware filters.

Author-supplied keywords

  • deep convolutional neural ne
  • edge aware filtering

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  • SGR: 84969785032
  • ISBN: 9781510810587
  • SCOPUS: 2-s2.0-84969785032
  • PUI: 610501372

Authors

  • Li Xu

  • Jimmy Sj Ren

  • Qiong Yan

  • Renjie Liao

  • Jiaya Jia

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