SERF: A Simple, Effective, Robust, and Fast Image Super-Resolver from Cascaded Linear Regression

Hu, Y; Wang, N; Tao, D; Gao, X; Li, X

SERF: A Simple, Effective, Robust, and Fast Image Super-Resolver from Cascaded Linear Regression

Hu, Y Wang, N Tao, D

Gao, X Li, X

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Image Processing, 2016, 25 (9), pp. 4091 - 4102
Issue Date:: 2016-09-01

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Field	Value	Language
dc.contributor.author	Hu, Y	en_US
dc.contributor.author	Wang, N	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Li, X	en_US
dc.date.issued	2016-09-01	en_US
dc.identifier.citation	IEEE Transactions on Image Processing, 2016, 25 (9), pp. 4091 - 4102	en_US
dc.identifier.issn	1057-7149	en_US
dc.identifier.uri	http://hdl.handle.net/10453/122803
dc.description.abstract	© 2016 IEEE. Example learning-based image super-resolution techniques estimate a high-resolution image from a low-resolution input image by relying on high- and low-resolution image pairs. An important issue for these techniques is how to model the relationship between high- and low-resolution image patches: most existing complex models either generalize hard to diverse natural images or require a lot of time for model training, while simple models have limited representation capability. In this paper, we propose a simple, effective, robust, and fast (SERF) image super-resolver for image super-resolution. The proposed super-resolver is based on a series of linear least squares functions, namely, cascaded linear regression. It has few parameters to control the model and is thus able to robustly adapt to different image data sets and experimental settings. The linear least square functions lead to closed form solutions and therefore achieve computationally efficient implementations. To effectively decrease these gaps, we group image patches into clusters via k-means algorithm and learn a linear regressor for each cluster at each iteration. The cascaded learning process gradually decreases the gap of high-frequency detail between the estimated high-resolution image patch and the ground truth image patch and simultaneously obtains the linear regression parameters. Experimental results show that the proposed method achieves superior performance with lower time consumption than the state-of-the-art methods.	en_US
dc.relation	http://purl.org/au-research/grants/arc/FT130101457
dc.relation	http://purl.org/au-research/grants/arc/DP140102164
dc.relation	http://purl.org/au-research/grants/arc/LE140100061
dc.relation.ispartof	IEEE Transactions on Image Processing	en_US
dc.relation.isbasedon	10.1109/TIP.2016.2580942	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	SERF: A Simple, Effective, Robust, and Fast Image Super-Resolver from Cascaded Linear Regression	en_US
dc.type	Journal Article
utslib.citation.volume	9	en_US
utslib.citation.volume	25	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.issue	9	en_US
pubs.publication-status	Published	en_US
pubs.volume	25	en_US

Abstract:

© 2016 IEEE. Example learning-based image super-resolution techniques estimate a high-resolution image from a low-resolution input image by relying on high- and low-resolution image pairs. An important issue for these techniques is how to model the relationship between high- and low-resolution image patches: most existing complex models either generalize hard to diverse natural images or require a lot of time for model training, while simple models have limited representation capability. In this paper, we propose a simple, effective, robust, and fast (SERF) image super-resolver for image super-resolution. The proposed super-resolver is based on a series of linear least squares functions, namely, cascaded linear regression. It has few parameters to control the model and is thus able to robustly adapt to different image data sets and experimental settings. The linear least square functions lead to closed form solutions and therefore achieve computationally efficient implementations. To effectively decrease these gaps, we group image patches into clusters via k-means algorithm and learn a linear regressor for each cluster at each iteration. The cascaded learning process gradually decreases the gap of high-frequency detail between the estimated high-resolution image patch and the ground truth image patch and simultaneously obtains the linear regression parameters. Experimental results show that the proposed method achieves superior performance with lower time consumption than the state-of-the-art methods.

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http://hdl.handle.net/10453/122803