| 基于L1/2正则化的超分辨率图像重建算法 |
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| 引用本文： | 王欢,王永革.基于L1/2正则化的超分辨率图像重建算法[J].计算机工程,2012,38(20):191-194. |
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| 作者姓名： | 王欢 王永革 |
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| 作者单位： | 北京航空航天大学数学与系统科学学院,北京,100191 |
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| 基金项目： | 国家自然科学基金资助项目(10801007);国家“973”计划基金资助项目(2010CB731900) |
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| 摘 要： | 为提高图像重建质量,研究超分辨率图像重建技术与稀疏表示理论,提出一种基于L1/2正则化的超分辨率图像重建算法.将L1/2正则化理论运用到字典学习中,利用学习得到的字典重建高分辨率图像.实验结果表明,该算法的图像重建效果优于基于L1正则化的超分辨率图像重建算法.
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| 关 键 词： | L1/2正则化 稀疏表示 超分辨率图像重建 K-SVD算法 字典学习 训练样本 |
| 收稿时间： | 2011-12-31 |
| 修稿时间： | 2012-02-26 |
Super-resolution Image Reconstruction Algorithm Based on L1/2 Regularization |
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| WANG Huan
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WANG Yong-ge.Super-resolution Image Reconstruction Algorithm Based on L1/2 Regularization[J].Computer Engineering,2012,38(20):191-194. |
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| Authors: | WANG Huan WANG Yong-ge |
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| Affiliation: | (School of Mathematics and Systems Science,Beihang University,Beijing 100191,China) |
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| Abstract: | In order to improve the image reconstruction quality,by studying the super-resolution image reconstruction technology and the theory of sparse representation,this paper proposes a super-resolution image reconstruction algorithm based on L1/2 regularization.It applies L1/2 regularization into dictionary learning,and reconstructs super-resolution images using learned dictionaries.Experimental results show that the reconstruction results in this paper are better than the results of super-resolution image reconstruction algorithm based on L1 regularization. |
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| Keywords: | L1/2 regularization sparse representation super-resolution image reconstruction K-SVD algorithm dictionary learning training sample |
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