|
|||||
|
|
| 基于聚类的自适应图像稀疏表示算法及其应用 | |
| 引用本文： | 徐健,常志国.基于聚类的自适应图像稀疏表示算法及其应用[J].光子学报,2014,40(2):316-320. |
| 作者姓名： | 徐健 常志国 |
| 作者单位： | （1 西安邮电学院 通信与信息工程学院,西安 710121)(2 西安交通大学 电子与信息工程学院,西安 710049)(3 长安大学 信息工程学院,西安 710064)(4 陕西省道路交通检测与装备工程技术研究中心,西安 710064） |
| 基金项目： | 中央高校基本科研业务费专项资金（No.CHD2009JC156）、西安邮电学院青年教师基金(No.ZL2010-21)和长安大学基础研究支持计划专项基金资助 |
| 摘 要： | 提出了一种针对一类图像进行稀疏表示的字典训练方法,并证明了该算法的收敛性.该算法的几何解释是,以最少的超平面来逼近样本所在的一小块球冠.算法流程为聚类每一步迭代所产生的余项,将聚类中心作为新的字典原子,令字典能够更适应于样本的稀疏表示.该算法与传统的字典训练方法相比具有适应性强,对训练样本规模和字典规模要求低,收敛速度快,算法复杂度低等特点.利用该算法训练得到的字典用于压缩感知、图像去噪等实验表明,该字典具有很好的效果. |
| 关 键 词： | 稀疏表示 聚类 压缩感知 字典 原子 稀疏度 |
| 收稿时间： | 2010-07-23 |
Self-adaptive Image Sparse Representation Algorithm Based on Clustering and Its Application |
|
| XU Jian,CHANG Zhi-guo.Self-adaptive Image Sparse Representation Algorithm Based on Clustering and Its Application[J].Acta Photonica Sinica,2014,40(2):316-320. | |
| Authors: | XU Jian CHANG Zhi-guo |
| Institution: | (1 School of Communication and Information Engineering,Xi′an University of Posts &|Telecommunications, Xi′an 710121,China) (2 School of |Electronic and information Engineering,Xi′an Jiaotong University,Xi′an 710049,China) (3 College of Information and Engineering,Chang'an University,Xi′an |710064,China) (4 Shaanxi Engineering and Technique Research Center for Road and Traffic Detection,Xi′an 710064,China) |
| Abstract: | A dictionary training algorithm was proposed for spare representation of images and its convergence was proved.The geometrical explanation of the algorithm is to approximate the hyperspherical cap with least hyperplanes.The algorithm clustered the error vectors of each step,and signed the cluster center as new atoms which made the dictionary more suitable for spare representation of samples.Compared with the traditional algorithm,the new one has higher adaptability,lower requirement of sample number and dictionary size,higher convergence rate,and lower complexity.Finally,the experiment of compressive sensing and denoising demonstrates that dictionary training by this algorithm has good effect. |
| Keywords: | Sparse representation Clustering compressive sensing Dictionary Atom Sparseness |
| 点击此处可从《光子学报》浏览原始摘要信息 | |
| 点击此处可从《光子学报》下载免费的PDF全文 | |