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Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Models for Patch-Based Image Restoration | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2009 Article ID 641804 12 pages doi 10.1155 2009 641804 Research Article Models for Patch-Based Image Restoration Mithun Das Gupta 1 Shyamsundar Rajaram 1 Nemanja Petrovic 2 and Thomas S. Huang1 1Beckman Institute Department of Electrical and Computer Engineering ECE University of Illinois at Urbana-Champaign UIUC IL 61801 USA 2Google Inc. NY 10011 USA Correspondence should be addressed to Mithun Das Gupta mdgupta@uiuc.edu Received 29 April 2008 Accepted 24 October 2008 Recommended by Simon Lucey 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. Copyright 2009 Mithun Das Gupta et al. 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. 1. Introduction Restoration is a neat showcase of ill-posedness of computer vision. Given a blurred image there can be several sharp natural images which when blurred will generate the .
