一种基于多尺度极值的快速自适应二维经验模式分解方法
作者:
作者单位:

1.大连东软信息学院智能与电子工程学院,大连,116023;2.海军大连舰艇学院军事海洋系,大连,116018;3.大连医科大学基础医学院,大连,116044

作者简介:

通讯作者:

基金项目:

国家自然科学基金(61471412)资助项目。


An Improved Fast Adaptive BEMD Method Based on Multi-scale Extrema
Author:
Affiliation:

1.School of Intelligence & Electronic Engineering, Dalian Neusoft University of Information, Dalian, 116023, China;2.Department of Military Oceanography, PLA Dalian Naval Academy, Dalian, 116018, China;3.School of Basic Medicine,Dalian Medical University, Dalian, 116044, China

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
    摘要:

    现有的二维经验模式分解(Bidimensional empirical mode decomposition, BEMD)算法在极值点查找、内蕴模式筛选和迭代过程中效率低、自适应性有待进一步提高,因此本文提出了一种基于多尺度极值的二维信号经验模式分解方法。首先给出二维多尺度极值二叉树结构的概念和建立方法,进而引出一个新的分解层数和滤波窗口大小的自适应确定原则,由此形成了改进的快速自适应二维经验模式分解方法。对自然图像和合成纹理图像分解的实验结果表明:与现有的快速自适应二维经验模式分解方法相比较,新方法的自适应性和效率都有明显提升。

    Abstract:

    The existing bidimensional empirical mode decomposition (BEMD) algorithms are inefficient in extrema searching, intrinsic mode functions sifting and iteration processing, moreover the adaptability needs to be further improved. Therefore, this paper proposes an improved BEMD method based on multi-scale extrema. Firstly, the concept and establishment method of bidimensional multi-scale local extrema binary tree are given, and then a new approach based on multi-scale extrema is presented to determine window sizes for order-statistics and smoothing filters. This method significantly improves the adaptability of multi-scale decomposition of two-dimensional signals, and also significantly improves the decomposition efficiency. Experimental results of natural image and synthetic texture image decomposition show that the proposed method has obvious advantages in adaptability and efficiency compared with the existing fast adaptive EBMD method.

    参考文献
    相似文献
    引证文献
引用本文

杨达,刘述田,徐冠雷,王晓炜.一种基于多尺度极值的快速自适应二维经验模式分解方法[J].数据采集与处理,2020,35(2):362-372

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
历史
  • 收稿日期:2019-06-24
  • 最后修改日期:2019-09-07
  • 录用日期:
  • 在线发布日期: 2020-04-30