An efficient example-based approach for image super-resolution - 文章详情页

作者：

Li, Xiaoguang (Li, Xiaoguang.) | Lam, Kin Man (Lam, Kin Man.) | Qiu, Guoping (Qiu, Guoping.) | Shen, Lansun (Shen, Lansun.) | Wang, Suyu (Wang, Suyu.)

收录：

EI Scopus

摘要：

A　novel　algorithm　for　image　super-resolution　with　class-specific　predictors　is　proposed　in　this　paper.　In　our　algorithm,　the　training　example　images　are　classified　into　several　classes,　and　each　patch　of　a　low-resolution　image　is　classified　into　one　of　these　classes.　Each　class　has　its　high-frequency　information　inferred　using　a　class-specific　predictor,　which　is　trained　via　the　training　samples　from　the　same　class.　In　this　paper,　two　different　types　of　training　sets　are　employed　to　investigate　the　impact　of　the　training　database　to　be　used.　Experimental　results　have　shown　the　superior　performance　of　our　method.　©　2008　IEEE.

关键词：

Neural networks Optical resolving power Image classification

作者机构：

[ 1 ] [Li, Xiaoguang]Signal and Information Processing Lab., Beijing University of Technology, Beijing, 100124, China
[ 2 ] [Li, Xiaoguang]Centre for Signal Processing, Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong, Hong Kong
[ 3 ] [Lam, Kin Man]Centre for Signal Processing, Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hong Kong, Hong Kong
[ 4 ] [Qiu, Guoping]Department of Computer Science, Nottingham University, United Kingdom
[ 5 ] [Shen, Lansun]Signal and Information Processing Lab., Beijing University of Technology, Beijing, 100124, China
[ 6 ] [Wang, Suyu]Signal and Information Processing Lab., Beijing University of Technology, Beijing, 100124, China

通讯作者信息：

电子邮件地址：

lxg@emails.bjut.edu.cn

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