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Image Super-Resolution Reconstruction based on Multi-Groups of Coupled Dictionary and Alternative Learning

by Sun Guangling, Li Guoqing, Jiang Xiaoqing
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 41 - Number 10
Year of Publication: 2012
Authors: Sun Guangling, Li Guoqing, Jiang Xiaoqing
10.5120/5577-7683

Sun Guangling, Li Guoqing, Jiang Xiaoqing . Image Super-Resolution Reconstruction based on Multi-Groups of Coupled Dictionary and Alternative Learning. International Journal of Computer Applications. 41, 10 ( March 2012), 22-31. DOI=10.5120/5577-7683

@article{ 10.5120/5577-7683,
author = { Sun Guangling, Li Guoqing, Jiang Xiaoqing },
title = { Image Super-Resolution Reconstruction based on Multi-Groups of Coupled Dictionary and Alternative Learning },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 41 },
number = { 10 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 22-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume41/number10/5577-7683/ },
doi = { 10.5120/5577-7683 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:29:15.257630+05:30
%A Sun Guangling
%A Li Guoqing
%A Jiang Xiaoqing
%T Image Super-Resolution Reconstruction based on Multi-Groups of Coupled Dictionary and Alternative Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 41
%N 10
%P 22-31
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A novel image super-resolution reconstruction framework based on multi-groups of coupled dictionary and alternative learning is investigated in this paper. In dictionary learning phase, each image of a training image set is taken as high resolution image (HRI), the reduced and re-enlarged result of HRI by interpolation is taken as low resolution image (LRI), and the difference between them is residual image. To obtain the mapping between residual and LRI, coupled dictionaries are learned from joint data composed of residual image patch and LRI patch features. Considering that distinguished texture and structural characteristics reflected in image patches and dictionary learning efficiency as well, multi-groups of coupled dictionary and alternative learning scheme are proposed. In reconstruction phase, LRI is obtained first. Then sparse representations and corresponding errors are calculated for each patch of the LRI by using low resolution component of each group of coupled dictionary. The residual component of coupled dictionary with minimum errors is applied to reconstruct the corresponding residual image patch. All such reconstructed residual patches compose a residual image. Finally, the residual image and the LRI are fused to produce an expected HRI. An experimental study is performed to establish that the proposed approach improves the super-resolution reconstruction quality.

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Index Terms

Computer Science
Information Sciences

Keywords

Super-resolution Sparse Representation Multi-dictionary Alternative Learning Principal Subspace Orthogonal Gaussian Mixture Model

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