Graph Regularized Dictionary for Single Image Super-resolution

被引：0
|
作者：
Zeng, Lei [1 ]
Ma, Shiqi [1 ]
Li, Xiaofeng [1 ]
机构：
[1] UESTC, Sch Commun & Informat Engn, Chengdu 611731, Sichuan, Peoples R China
来源：
2013 INTERNATIONAL CONFERENCE ON COMPUTATIONAL PROBLEM-SOLVING (ICCP) | 2013年
关键词：
graph regularized dictionary; super resolution; sparse representation; joint dictionary training;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Super-resolution (SR) for single image is wild used in image processing areas. The learning-based methods use the co-trained dictionaries which contain low resolution and corresponding high resolution images to conduct SR. In this paper, a new dictionary for SR is proposed which adds the graph information between patches. Simulation results show that our scheme improved the dictionary and outperforms the existing classic SR algorithms in both subjective visually and quantitative evaluations.
引用
收藏
页码：257 / 259
页数：3
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