Image super-resolution based on image adaptive decomposition

被引:0
|
作者
Xie, Qiwei [1 ]
Wang, Haiyan [1 ]
Shen, Lijun [2 ]
Chen, Xi [2 ]
Han, Hua [2 ]
机构
[1] Jiangsu Prov Inst Qual & Safety Engn, 3 Wenyuan Rd, Nanjing 210046, Peoples R China
[2] Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
关键词
Empirical Mode Decomposition; Morphological Component Analysis; Image Super-resolution; Gaussian Mixture Model; MORPHOLOGICAL COMPONENT ANALYSIS; EMPIRICAL MODE DECOMPOSITION;
D O I
10.1117/12.911893
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 image super-resolution algorithm based on Gaussian Mixture Model (GMM) and a new adaptive image decomposition algorithm. The new image decomposition algorithm uses local extreme of image to extract the cartoon and oscillating part of image. In this paper, we first decompose an image into oscillating and piecewise smooth (cartoon) parts, then enlarge the cartoon part with interpolation. Because GMM accurately characterizes the oscillating part, we specify it as the prior distribution and then formulate the image super-resolution problem as a constrained optimization problem to acquire the enlarged texture part and finally we obtain a fine result.
引用
收藏
页数:8
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