Models for Patch-Based Image Restoration

<p>Abstract</p> <p>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...

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Bibliographic Details
Main Authors: Petrovic Nemanja, Das Gupta Mithun, Rajaram Shyamsundar, Huang ThomasS
Format: Article
Language:English
Published: SpringerOpen 2009-01-01
Series:EURASIP Journal on Image and Video Processing
Online Access:http://jivp.eurasipjournals.com/content/2009/641804
Description
Summary:<p>Abstract</p> <p>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.</p>
ISSN:1687-5176
1687-5281