IMAGE SUPER-RESOLUTION VIA NON-LOCAL STEERING KERNEL REGRESSION REGULARIZATION

被引：0
|
作者：
Zhang, Kaibing [1 ]
Gao, Xinbo [2 ]
Tao, Dacheng [3 ,4 ]
Li, Xuelong [5 ]
机构：
[1] Hubei Engn Univ, Sch Comp & Informat Sci, Xiaogan 432000, Peoples R China
[2] Xidian Univ, Sch Elect Engn, Xian 710071, Peoples R China
[3] Univ Technol Sydney, QCIS, Sydney, NSW 2007, Australia
[4] Univ Technol Sydney, FEIT, Sydney, NSW 2007, Australia
[5] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
来源：
2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013) | 2013年
关键词：
Image super-resolution; local structure regularity; non-local self-similarity; steering kernel regression;
D O I：
暂无
中图分类号：
TB8 [摄影技术];
学科分类号：
0804 ;
摘要：
In this paper, we employ the non-local steering kernel regression to construct an effective regularization term for the single image super-resolution problem. The proposed method seamlessly integrates the properties of local structural regularity and non-local self-similarity existing in natural images, and solves a least squares minimization problem for obtaining the desired high-resolution image. Extensive experimental results on both simulated and real low-resolution images demonstrate that the proposed method can restore compelling results with sharp edges and fine textures.
引用
收藏
页码：943 / 946
页数：4
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