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Models for Patch-Based Image Restoration

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Abstract

We present a supervised learning approach for object-category specific restoration, recognition, and segmentation of images which are blurred using an unknown kernel. The novelty of this work is a multilayer graphical model which unifies the low-level vision task of restoration and the high-level vision task of recognition in a cooperative framework. The graphical model is an interconnected two-layer Markov random field. The restoration layer accounts for the compatibility between sharp and blurred images and models the association between adjacent patches in the sharp image. The recognition layer encodes the entity class and its location in the underlying scene. The potentials are represented using nonparametric kernel densities and are learnt from training data. Inference is performed using nonparametric belief propagation. Experiments demonstrate the effectiveness of our model for the restoration and recognition of blurred license plates as well as face images.

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Correspondence to Mithun Das Gupta.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Das Gupta, M., Rajaram, S., Petrovic, N. et al. Models for Patch-Based Image Restoration. J Image Video Proc 2009, 641804 (2009) doi:10.1155/2009/641804

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Keywords

  • Face Image
  • Markov Random Field
  • Vision Task
  • License Plate
  • Nonparametric Kernel