One New Method on Image Super-resolution Reconstruction

被引：0

作者：

Zhang, Lili ^{[1
]}

Wang, Xin ^{[1
]}

Li, Chenming ^{[1
]}

Li, Qi ^{[1
]}

Li, Xiaofang ^{[2
]}

机构：

[1] Hohai Univ, Coll Comp & Informat Engn, Nanjing 211100, Peoples R China

[2] Changzhou Inst Technol, Coll Comp & Informat Engn, Changzhou 213002, Peoples R China

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION | 2015年

关键词：

resolution reconstruction; sparse representation; multi-features extraction; sparse prior;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

According to the image quality degradation in super-resolution reconstruction, we present a new algorithm for a single image super-resolution reconstruction to improve the image resolution. Considering the limitations that the extracted features of low-resolution image can not adapt to image direction changes, we propose a new feature-extracted method, and we build a new global optimization framework with analysis sparse prior based on the multiple extracted features, joint dictionary learning. Experiments show the effectiveness of our method.

引用

页码：79 / 82

页数：4

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