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	Image super-resolution based on data-driven gaussian process regression
	Qu, Yan-Yun ; Liao, Meng-Jie ; Zhou, Yan-Wen ; Fang, Tian-Zhu ; Lin, Li ; Zhang, Hai-Ying ; Qu YY(曲延云)
	2013
关键词	Covariance matrix Gaussian distribution Gaussian noise (electronic) Regression analysis
英文摘要	Conference Name:4th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2013. Conference Address: Beijing, China. Time:July 31, 2013 - August 2, 2013.; In this paper, we aim at producing the super-resolution image from a single low-resolution image based on Gaussian Process regression. Gaussian Processes provide a framework for deriving regression techniques with explicit uncertainty models. Super resolution can be transformed into a regression problem. We show how Gaussian Processes with covariance functions can be used for image super-resolution. Furthermore, considering that the training data have greatly effect on the super-resolution performance and the unsuitable training data would result in unexpected details, we adopt a data-driven scheme to learn a regression map for each query patch. There are two advantages of our approach: 1) we establish a map between the low-resolution space and the high-resolution space independent of a specified regression function; 2) the data-driven learning scheme improves the super-resolution performance. We estimate our approach on the popular testing images which are used in other super-resolution literatures, and the results demonstrate that our approach is efficient and it manifests a high-quality performance compared with several popular super-resolution methods. ? 2013 Springer-Verlag Berlin Heidelberg.
语种	英语
出处	http://dx.doi.org/10.1007/978-3-642-42057-3-65
出版者	Springer Verlag
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/86553]
专题	信息技术－会议论文
推荐引用方式 GB/T 7714	Qu, Yan-Yun,Liao, Meng-Jie,Zhou, Yan-Wen,et al. Image super-resolution based on data-driven gaussian process regression. 2013-01-01.

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