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Discriminative Indexing for Probabilistic Image Patch Priors

  • Yan Wang
  • Sunghyun Cho
  • Jue Wang
  • Shih-Fu Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8692)

Abstract

Newly emerged probabilistic image patch priors, such as Expected Patch Log-Likelihood (EPLL), have shown excellent performance on image restoration tasks, especially deconvolution, due to its rich expressiveness. However, its applicability is limited by the heavy computation involved in the associated optimization process. Inspired by the recent advances on using regression trees to index priors defined on a Conditional Random Field, we propose a novel discriminative indexing approach on patch-based priors to expedite the optimization process. Specifically, we propose an efficient tree indexing structure for EPLL, and overcome its training tractability challenges in high-dimensional spaces by utilizing special structures of the prior. Experimental results show that our approach accelerates state-of-the-art EPLL-based deconvolution methods by up to 40 times, with very little quality compromise.

Keywords

Gaussian Mixture Model Markov Random Field Image Patch Conditional Random Field Tree Depth 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yan Wang
    • 1
  • Sunghyun Cho
    • 2
  • Jue Wang
    • 2
  • Shih-Fu Chang
    • 1
  1. 1.Dept. of Electrical EngineeringColumbia UniversityUSA
  2. 2.Adobe ResearchUSA

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