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Open Access

Models for Patch-Based Image Restoration

  • Mithun Das Gupta1Email author,
  • Shyamsundar Rajaram1,
  • Nemanja Petrovic2 and
  • Thomas S. Huang1
EURASIP Journal on Image and Video Processing20092009:641804

Received: 29 April 2008

Accepted: 24 October 2008

Published: 29 January 2009


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.


Face ImageMarkov Random FieldVision TaskLicense PlateNonparametric Kernel

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Authors’ Affiliations

Beckman Institute, Department of Electrical and Computer Engineering (ECE), University of Illinois at Urbana-Champaign (UIUC), USA
Google Inc., USA


© Mithun Das Gupta et al. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.