| 一种改进的基于K-SVD字典的图像修复算法 |
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| 引用本文： | 黄江林,刘红,陶少杰.一种改进的基于K-SVD字典的图像修复算法[J].安徽大学学报(自然科学版),2013(3). |
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| 作者姓名： | 黄江林 刘红 陶少杰 |
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| 作者单位： | 安徽大学计算智能与信号处理教育部重点实验室; |
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| 基金项目： | 安徽省教育厅自然科学基金重点资助项目(KJ2009A60);安徽大学博士科研启动基金资助项目(33190049);安徽大学“211工程”学术创新团队基金资助项目(KJTD007A) |
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| 摘 要： | 提出一种改进的基于K-SVD字典的图像修复算法.该算法基于稀疏表示,利用待修复图像内的有效信息,以不重叠像素的方式提取图像块,采用模糊C均值聚类算法对图像块进行聚类,并使用K-SVD算法分别对各类图像块进行训练,得到与各类图像块相适应的字典,重建图像块,修复受损图像.实验结果表明,该算法能提高图像的修复质量和图像的峰值性噪比,且均方根误差较小.
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| 关 键 词： | 稀疏表示 图像修复 K-SVD 训练字典 模糊C均值聚类 |
An improved inpainting algorithm based on K-SVD dictionary |
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| Abstract: | This paper proposed an improved inpainting algorithm which was based on K-SVD dictionary.The algorithm based on sparse representation used valid image information,extracted image blocks with no overlaps,adopted fuzzy C-means clustering algorithm to cluster image blocks,and used K-SVD algorithm to train dictionary for each class,obtained dictionary which was adapted to each class,reconstructed image blocks,restored damaged image.Experimental results showed that this algorithm repaired damaged image well,had a smaller root mean square error,and improved peak signal-to-noise ratio. |
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| Keywords: | sparse representation image inpainting K-SVD training dictionary fuzzy C-means clustering |
| 本文献已被 CNKI 等数据库收录！ |
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