Super-Resolution using Regularized Orthogonal Matching Pursuit based on Compressed Sensing Theory in the Wavelet Domain

被引：7
|
作者：
Fan, Na [1 ]
机构：
[1] E China Normal Univ, Dept Elect Engn, Shanghai 200241, Peoples R China
来源：
PROCEEDINGS OF THE 2009 SIXTH INTERNATIONAL CONFERENCE ON COMPUTER GRAPHICS, IMAGING AND VISUALIZATION | 2009年
关键词：
SIGNAL RECOVERY;
D O I：
10.1109/CGIV.2009.90
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A wavelet based compressed sensing Super Resolution algorithm is developed, in which the energy function optimization is approximated numerically via the Regularized Orthogonal Matching Pursuit. The proposed algorithm works well with a smaller quantity of training image patches and outputs images with satisfactory subjective quality. It is tested on classical images commonly adopted by Super Resolution researchers with both generic and specialized training sets for comparison with other popular commercial software and state-of-the-art methods. Experiments demonstrate that, the proposed algorithm is competitive among contemporary Super Resolution methods.
引用
收藏
页码：349 / 354
页数：6
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