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字典学习模型、算法及其应用研究进展

练秋生 石保顺 陈书贞

练秋生, 石保顺, 陈书贞. 字典学习模型、算法及其应用研究进展. 自动化学报, 2015, 41(2): 240-260. doi: 10.16383/j.aas.2015.c140252
引用本文: 练秋生, 石保顺, 陈书贞. 字典学习模型、算法及其应用研究进展. 自动化学报, 2015, 41(2): 240-260. doi: 10.16383/j.aas.2015.c140252
LIAN Qiu-Sheng, SHI Bao-Shun, CHEN Shu-Zhen. Research Advances on Dictionary Learning Models, Algorithms and Applications. ACTA AUTOMATICA SINICA, 2015, 41(2): 240-260. doi: 10.16383/j.aas.2015.c140252
Citation: LIAN Qiu-Sheng, SHI Bao-Shun, CHEN Shu-Zhen. Research Advances on Dictionary Learning Models, Algorithms and Applications. ACTA AUTOMATICA SINICA, 2015, 41(2): 240-260. doi: 10.16383/j.aas.2015.c140252

字典学习模型、算法及其应用研究进展

doi: 10.16383/j.aas.2015.c140252
基金项目: 

国家自然科学基金(61471313),河北省自然科学基金(F2014203076)资助

详细信息
    作者简介:

    石保顺 燕山大学信息科学与工程学院博士研究生. 主要研究方向为图像处理,盲压缩感知, 字典学习.E-mail: shibaoshun1989@163.com

    陈书贞 燕山大学信息科学与工程学院副教授. 主要研究方向为图像处理, 压缩感知及生物识别.E-mail: chen sz818@163.com

    通讯作者:

    练秋生 燕山大学信息科学与工程学院教授. 主要研究方向为图像处理, 稀疏表示, 压缩感知及多尺度几何分析. 本文通信作者. E-mail: lianqs@ysu.edu.cn

Research Advances on Dictionary Learning Models, Algorithms and Applications

Funds: 

Supported by National Natural Science Foundation of China (61471313), and Natural Science Foundation of Hebei Province (F2014203076)

  • 摘要: 稀疏表示模型常利用训练样本学习过完备字典, 旨在获得信号的冗余稀疏表示. 设计简单、高效、通用性强的字典学习算法是目前的主要研究方向之一, 也是信息领域的研究热点. 基于综合稀疏模型的字典学习方法已经广泛应用于图像分类、图像去噪、图像超分辨率和压缩成像等领域. 近些年来, 解析稀疏模型、盲字典模型和信息复杂度模型等新模型的出现丰富了字典学习理论, 使得更广泛类型的信号能够被简单性描述. 本文详细介绍了综合字典、解析字典、盲字典和基于信息复杂度字典学习的基本模型及其算法, 阐述了字典学习的典型应用, 指出了字典学习的进一步研究方向.
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出版历程
  • 收稿日期:  2014-04-14
  • 修回日期:  2014-10-12
  • 刊出日期:  2015-02-20

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