图像多尺度几何分析域隐马尔可夫树模型研究进展

王相海; 宋若曦; 曲思洁; 穆振华; 宋传鸣

doi:10.12263/DZXB.20200821

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图像多尺度几何分析域隐马尔可夫树模型研究进展

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- 图像多尺度几何分析域隐马尔可夫树模型研究进展
- Advance in Multiscale Geometric Analysis Image Hidden Markov Tree Model
- 电子学报 2022年50卷第1期页码：238-249
- 作者机构：
  
  1.辽宁师范大学地理科学学院，辽宁大连 116029
  2.辽宁师范大学计算机科学与信息技术学院，辽宁大连 116081
  3.辽宁师范大学数学学院，辽宁大连 116029
- 作者简介：
  
  [ "王相海男，1965年1月出生于吉林省汪清县.现为辽宁师范大学计算机与信息技术学院、地理科学学院教授、博士生导师.主要研究方向为多媒体信息处及遥感影像处理. E-mail:xhwang@lnnu.edu.cn" ]
  [ "宋若曦女，1994年12月出生于内蒙古自治区通辽市.现为辽宁师范大学地理科学学院地图学与地理信息系统专业博士研究生.主要研究方向为高光谱影像处理. E-mail:srxlnnu@outlook.com" ]
- 基金信息：
  
  国家自然科学基金(41971388;41671439);辽宁省高等学校创新团队支持计划(LT2017013)
- DOI：10.12263/DZXB.20200821
  中图分类号： TP391;
- 收稿：2020-08-03，
  
  修回：2020-10-29，
  
  纸质出版：2022-01-25
- 稿件说明：
移动端阅览
王相海,宋若曦,曲思洁等.图像多尺度几何分析域隐马尔可夫树模型研究进展[J].电子学报,2022,50(01):238-249.

WANG Xiang-hai,SONG Ruo-xi,QU Si-jie,et al.Advance in Multiscale Geometric Analysis Image Hidden Markov Tree Model[J].ACTA ELECTRONICA SINICA,2022,50(01):238-249.
王相海,宋若曦,曲思洁等.图像多尺度几何分析域隐马尔可夫树模型研究进展[J].电子学报,2022,50(01):238-249. DOI： 10.12263/DZXB.20200821.

WANG Xiang-hai,SONG Ruo-xi,QU Si-jie,et al.Advance in Multiscale Geometric Analysis Image Hidden Markov Tree Model[J].ACTA ELECTRONICA SINICA,2022,50(01):238-249. DOI： 10.12263/DZXB.20200821.

摘要

多尺度几何分析（Multiscale Geometric Analysis，MGA）为图像的高维奇异特性提供了一种更优、更稀疏的表示方法，从而为更好地捕捉图像中的多方向边缘和纹理特性提供了有效的支撑.图像MGA域隐马尔可夫树模型（Hidden Markov Tree，HMT）成功地对图像多尺度变换系数的统计特性及系数间的相关性进行刻画，为进一步挖掘图像更深层次特性提供了重要途径，在很大程度上提升了MGA在图像处理领域的有效性.本文对图像MGA域HMT模型的研究进展进行综述.先对传统MGA域HMT模型的发展进行分析和讨论，对其构建的一般过程进行了形式化描述；在此基础上，归纳了传统MGA域HMT模型存在的一些关键问题，并以此为导向对MGA变换域HMT模型的研究进展进行了分析和讨论；最后对MGA域HMT模型未来的发展情况进行了展望.

Abstract

Multiscale geometric analysis(MGA) provides a better representation for the high dimensional singular feature of images

which provides a better support for capturing the multidirectional edges and textures of the image. The image hidden Markov tree(HMT) model can efficiently depict the statistical properties of the image multiscale transform coefficients and the correlation among them

which helps to further utilizing the deep features of the image. The success of MGA-HMT greatly improved the effectiveness of MGA in the field of image processing. This paper reviews the research progress of MGA-HMT model. Firstly

the development of the traditional MGA-HMT model is analyzed and discussed

the general process of its construction is defined. Based on this

some key issues of the traditional MGA-HMT model are summarized. Guided by these issues

the research progress of the MGA-HMT has been further studied. Finally

the future development of the MGA-HMT is prospected.

关键词

Keywords

references

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