Single image super-resolution based on support vector regression

被引：0
|
作者：
Li, Dalong [1 ]
Simske, Steven [1 ]
Mersereau, Russell M. [2 ]
机构：
[1] Hewlett Packard Labs, Digital Printing & Imaging Lab, Ft Collins, CO 80528 USA
[2] Georgia Inst Tech, Sch Elect & Comp Engn, Atlanta, GA 30332 USA
来源：
2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6 | 2007年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Motivated by the success of support vector regression (SVR) in blind image deconvolution, we apply SVR to single-frame super-resolution. Initial results show that even when trained on as little as a single image, SVR is able to learn a generally applicable model that can super-resolve dissimilar images.
引用
收藏
页码：2903 / +
页数：2
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