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A learning-based method for image super-resolution from zoomed observations

DSpace at IIT Bombay

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Title A learning-based method for image super-resolution from zoomed observations
 
Creator CHAUDHURI, SUBHASIS
JOSHI, MV
PANUGANTI, R
 
Subject markov processes
image quality
learning systems
regression analysis
 
Description We propose a technique for super-resolution imaging of a scene from observations at different camera zooms. Given a sequence of images with different zoom factors of a static scene, we obtain a picture of the entire scene at a resolution corresponding to the most zoomed observation. The high-resolution image is modeled through appropriate parameterization, and the parameters are learned from the most zoomed observation. Assuming a homogeneity of the high-resolution field, the learned model is used as a prior while super-resolving the scene. We suggest the use of either a Markov random field (MRF) or an simultaneous autoregressive (SAR) model to parameterize the field based on the computation one can afford. We substantiate the suitability of the proposed method through a large number of experimentations on both simulated and real data.
 
Publisher IEEE
 
Date 2008-11-24T05:32:03Z
2011-11-25T12:45:18Z
2011-12-26T13:08:54Z
2011-12-27T05:34:17Z
2008-11-24T05:32:03Z
2011-11-25T12:45:18Z
2011-12-26T13:08:54Z
2011-12-27T05:34:17Z
2005
 
Type Article
 
Identifier IEEE Transactions on Systems, Man, and Cybernetics, Part B 35(3), 527-37
1083-4419
http://dx.doi.org/10.1109/TSMCB.2005.846647
http://hdl.handle.net/10054/109
http://dspace.library.iitb.ac.in/xmlui/handle/10054/109
 
Language en_US