MULTI-SCALE NON-LOCAL KERNEL REGRESSION FOR SUPER RESOLUTION

被引：0
|
作者：
Zhang, Haichao [1 ,2 ]
Yang, Jianchao [2 ]
Zhang, Yanning [1 ]
Huang, Thomas S. [2 ]
机构：
[1] Northwest Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China
[2] Univ Illinois, Beckman Inst, Champaign, IL USA
来源：
2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | 2011年
关键词：
Non-Local Kernel Regression; multi-scale self-similarity; local structural regularity; super resolution; image restoration;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we propose an extension of the Non-Local Kernel Regression (NL-KR) method and apply it to super-resolution (SR) tasks. The proposed method extends NL-KR via generalizing the self-similarity from single-scale to multi-scale, and propose an effective SR algorithm using the proposed multi-scale NL-KR model. Experimental results on both synthetic and real images demonstrate the effectiveness of the proposed method.
引用
收藏
页码：1353 / 1356
页数：4
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