GradientShop: A Gradient-Domain Optimization Framework for Image and Video Filtering

  • Bhat P
  • Zitnick C
  • Cohen M
 et al. 
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Abstract

We present an optimization framework for exploring gradient-domain solutions for image and video processing. The proposed framework unifies many of the key ideas in the gradient-domain literature under a single optimization formulation. Our hope is that this generalized framework will allowthe reader to quickly gain a general understanding of the field and contribute new ideas of their own. We propose a novel metric for measuring local gradient saliency that identifies salient gradients that give rise to long, coherent edges, even when the individual gradients are faint.We present a general weighting scheme for gradient constraints that improves the visual appearance of results.We also provide a solution for applying gradient-domain filters to videos and video streams in a coherent manner. Finally, we demonstrate the utility of our formulation in creating effective yet simple to implement solutions for various image-processing tasks. To exercise our formulation we have created a new saliency-based sharpen filter and a pseudo image-relighting application.We also revisit and improve upon previously defined filters such as nonphotorealistic rendering, image deblocking, and sparse data interpolation over images (e.g., colorization using optimization).

Author-supplied keywords

  • Gradient domain
  • NPR
  • deblocking
  • relighting
  • sparse data interpolation

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Authors

  • Pravin Bhat

  • C. Lawrence Zitnick

  • Michael Cohen

  • Brian Curless

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