Models for patch based image restoration

Mithun Das Gupta, Shyamsundar Rajaram, Nemanja Petrovic, Thomas S. Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper we present a supervised learning approach for object-category specific restoration, recognition and segmentation of images which are blurred using an unknown kernel. The feature of this work is a multi layer graphical model which unifies the low level vision task of restoration, and the high level vision task of recognition in a cooperative framework. Proposed graphical model is an interconnected two layer Markov Random Field. The restoration layer accounts for the compatibility between sharp and blurred patches, and models the association between adjacent patches in the sharp image. The recognition layer encodes the patch location and class. The potentials are represented using non-parametric kernel densities and are leamt from the training data. Inference is performed using non-parametric belief propagation. We propose a similar model for super-resolution from multiple frames, and suggest the use of ordinal regression for sub-pixel shift estimation to address the registration issues. Experiments demonstrate the effectiveness of proposed models for the restoration and recognition of blurred license plate and face images.

Original languageEnglish (US)
Title of host publication2006 Conference on Computer Vision and Pattern Recognition Workshop
StatePublished - Dec 21 2006
Event2006 Conference on Computer Vision and Pattern Recognition Workshops - New York, NY, United States
Duration: Jun 17 2006Jun 22 2006

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919


Other2006 Conference on Computer Vision and Pattern Recognition Workshops
Country/TerritoryUnited States
CityNew York, NY

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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