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基于L1/2正则化的超分辨率图像重建算法
Super-resolution Image Reconstruction Algorithm Based on L1/2 Regularization
【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。
【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.
【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;
【Key words】 L1/2 regularization; sparse representation; super-resolution image reconstruction; K-SVD algorithm; dictionary learning; training sample;
【Key words】 L1/2 regularization; sparse representation; super-resolution image reconstruction; K-SVD algorithm; dictionary learning; training sample;
【基金】 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)
- 【文献出处】 计算机工程 ,Computer Engineering , 编辑部邮箱 ,2012年20期
- 【分类号】TP391.41
- 【被引频次】22
- 【下载频次】415