[1]孙君顶,赵慧慧.图像稀疏表示及其在图像处理中的应用[J].红外技术,2014,36(7):533-537.[doi:10.11846/j.issn.1001_8891.201407004]
 SUN Jun-ding,ZHAO Hui-hui.Sparse Representation and Applications in Image Processing[J].Infrared Technology,2014,36(7):533-537.[doi:10.11846/j.issn.1001_8891.201407004]
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图像稀疏表示及其在图像处理中的应用
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
36卷
期数:
2014年7期
页码:
533-537
栏目:
出版日期:
2014-07-20

文章信息/Info

Title:
Sparse Representation and Applications in Image Processing
文章编号:
1001-8891(2014)07-0533-05
作者:
孙君顶赵慧慧
河南理工大学计算机科学与技术学院
Author(s):
SUN Jun-dingZHAO Hui-hui
School of Computer Science and technology, Henan Polytechnic University
关键词:
稀疏表示稀疏分解字典学习图像处理
Keywords:
sparse representationsparse decompositiondictionary learningimage processing
分类号:
TP391
DOI:
10.11846/j.issn.1001_8891.201407004
文献标志码:
A
摘要:
作为图像的一种高效表示方法,近年来,图像稀疏表示技术得到了广泛深入研究,目前已被广泛应用于图像处理领域。在分析图像稀疏表示模型的基础上,针对稀疏表示模型的两个核心问题稀疏分解与字典构造进行了详细讨论,综述了目前典型的稀疏分解方法与字典构造方法。在此基础上,对稀疏表示在图像去噪、图像修复、人脸识别及压缩感知等图像处理领域中的应用进行了总结。最后,讨论了目前图像稀疏表示研究中存在的问题,并指出了进一步的研究方向。
Abstract:
As an effective image representation method, sparse representation technique has been extensively studied and it has been widely used in the field of image processing. Based on the analysis of the sparse representation models in the paper, the key problems of the models are discussed in detail including sparse decomposition and dictionary learning. Then, the applications of sparse representation in the fields of image denoising, image inpainting, face recognition, and compressed sensing are summarized in detail. Finally, the problems in research of sparse representation are discussed and the research direction is also given in the paper.

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备注/Memo

备注/Memo:
收稿日期:2013-12-17;修订日期2014-02-26.
作者简介:孙君顶(1975-),男,河南邓州人,副教授,硕士研究生导师,主要研究领域为图像处理与模式识别技术。
基金项目:河南省骨干教师资助计划(2010GGJS-059);河南省国际合作项目(134300510057);河南省基础与前沿基金(112300410281);“图像处理与图像通信”江苏省重点实验室基金(LBEK2011002)

更新日期/Last Update: 2014-07-24