Mixed lp/l1 Norm Minimization Approach to Intra-Frame Super-Resolution

被引：1
|
作者：
Shimada, Kazuma [1 ]
Konishi, Katsumi [2 ]
Uruma, Kazunori [1 ]
Takahashi, Tomohiro [1 ]
Furukawa, Toshihiro [1 ]
机构：
[1] Tokyo Univ Sci, Grad Sch Engn, Tokyo 1628601, Japan
[2] Kogakuin Univ, Dept Comp Sci, Tokyo 1638677, Japan
来源：
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | 2014年 / E97D卷 / 10期
关键词：
super-resolution; sparse optimization;
D O I：
10.1587/transinf.2014EDL8086
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper deals with the problem of reconstructing a high-resolution digital image from a single low-resolution digital image and proposes a new intra-frame super-resolution algorithm based on the mixed l(p)/l(1) norm minimization. Introducing some assumptions, this paper formulates the super-resolution problem as a mixed l(0)/l(1) norm minimization and relaxes the l(0) norm term to the l(p) norm to avoid ill-posedness. A heuristic iterative algorithm is proposed based on the iterative reweighted least squares (IRLS). Numerical examples show that the proposed algorithm achieves super-resolution efficiently.
引用
收藏
页码：2814 / 2817
页数：4
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