Record Details

Single-frame image super-resolution through contourlet learning

DSpace at IIT Bombay

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Field Value
 
Title Single-frame image super-resolution through contourlet learning
 
Creator JIJI, CV
CHAUDHURI, S
 
Subject wavelet coefficients
filter banks
reconstruction
resolution
multisensors
restoration
algorithms
design
limits
 
Description We propose a learning-based, single-image super-resolution reconstruction technique using the contourlet transform, which is capable of capturing the smoothness along contours making use of directional decompositions. The contourlet coefficients at finer scales of the unknown high-resolution image are learned locally from a set of high-resolution training images, the inverse contourlet transform of which recovers the super-resolved image. In effect, we learn the high-resolution representation of an oriented edge primitive from the training data. Our experiments show that the proposed approach outperforms standard interpolation techniques as well as a standard ( Cartesian) wavelet-based learning both visually and in terms of the PSNR values, especially for images with arbitrarily oriented edges. Copyright (C) 2006 Hindawi Publishing Corporation. .
 
Publisher HINDAWI PUBLISHING CORPORATION
 
Date 2011-07-31T07:25:13Z
2011-12-26T12:52:53Z
2011-12-27T05:39:50Z
2011-07-31T07:25:13Z
2011-12-26T12:52:53Z
2011-12-27T05:39:50Z
2006
 
Type Article
 
Identifier EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING, (), -
1110-8657
http://dx.doi.org/10.1155/ASP/2006/73767
http://dspace.library.iitb.ac.in/xmlui/handle/10054/8028
http://hdl.handle.net/10054/8028
 
Language en