基于形态学滤波的快速多通道图像EMD

胡建平; 杜影; 谢琪; 王小超; 张道畅

doi:10.13700/j.bh.1001-5965.2020.0118

基于形态学滤波的快速多通道图像EMD

doi: 10.13700/j.bh.1001-5965.2020.0118

胡建平^{1, 2},
杜影¹,
谢琪¹,
王小超^{2, 3, ,},
张道畅¹

1.
东北电力大学理学院, 吉林 132012
2.
北京航空航天大学虚拟现实技术与系统国家重点实验室, 北京 100083
3.
天津工业大学数学科学学院, 天津 300387

基金项目:

国家自然科学基金 61672149

国家自然科学基金 61602341

国家自然科学基金 11901079

吉林省教育厅“十三五”科学技术研究规划项目 JJKH20190690KJ

北京航空航天大学虚拟现实技术与系统国家重点实验室开放基金 BUAA-VR-16KF-23

北京航空航天大学虚拟现实技术与系统国家重点实验室开放基金 BUAA-VR-17KF-04

详细信息

作者简介:
胡建平男, 博士, 教授。主要研究方向:计算机图形学、图像处理

王小超男, 博士, 副教授。主要研究方向:计算几何、图形图像处理

通讯作者:
王小超, E-mail: wangxiaochao18@163.com

中图分类号: TP391
计量
- 文章访问数: 757
- HTML全文浏览量: 196
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- 被引次数: 0
出版历程
- 收稿日期: 2020-04-01
- 录用日期: 2020-06-05
- 网络出版日期: 2020-11-20

A fast EMD for multi-channel images based on morphological filter

HU Jianping^{1, 2},
DU Ying¹,
XIE Qi¹,
WANG Xiaochao^{2, 3
, ,},
ZHANG Daochang¹

1.
School of Science, Northeast Electric Power University, Jilin 132012, China
2.
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100083, China
3.
School of Mathematical Sciences, Tiangong University, Tianjin 300387, China

Funds:

National Natural Science Foundation of China 61672149

National Natural Science Foundation of China 61602341

National Natural Science Foundation of China 11901079

"Thirteenth Five-Year Plan" Science and Technology Project of Education Department of Jilin Province JJKH20190690KJ

The Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University BUAA-VR-16KF-23

The Open Funding Project of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University BUAA-VR-17KF-04

More Information

Corresponding author: WANG Xiaochao, E-mail: wangxiaochao18@163.com

摘要

摘要:
为了提高多通道图像经验模态分解（EMD）方法分解的效率，提出了一种基于形态学滤波的快速多通道图像EMD方法。利用形态学膨胀和腐蚀滤波可以计算图像上下包络这一重要特性，实现了多通道图像EMD的快速计算，形态学滤波窗口大小由各通道图像的平均极值距离确定，将一幅多通道图像自适应分解为若干个尺度从细到粗的内蕴模态函数（IMF）图像和一个体现图像整体变化趋势的余量。大量的实验结果与对比显示，所提方法不但能够加快EMD方法分解的速度，而且也能够有效地对多通道图像进行自适应分解。通过在图像融合和图像水印中的应用及大量的实验比较，说明了所提方法能够方便快捷地投入到具体的图像处理任务中。
- 经验模态分解(EMD) /
- 顺序统计滤波 /
- 形态学滤波 /
- 内蕴模态图像 /
- 图像处理
Abstract:
In order to improve the efficiency of the existing multi-channel image Empirical Mode Decomposition (EMD) methods, this paper presents a fast multi-channel image EMD method based on morphological filter. It uses the morphological expansion and erosion filters to compute the upper and lower envelopes of an image, which can accelerate the implementation of EMD for multi-channel images. The size of the morphological filter window is determined by the average extremum distance of each channel image. The proposed EMD method can decompose a multi-channel image adaptively into several Intrinsic Mode Function (IMF) images with scales from fine to coarse, and a residue representing the overall change trend of the image. A number of experimental results and comparisons show that the proposed method can not only accelerate the decomposition of EMD method, but also generate a good multi-scale adaptive decomposition for a multi-channel image. Its applications in image fusion and image watermarking and many experimental comparisons show that the proposed EMD method can be used to solve some specific image processing tasks conveniently and promptly.
- Empirical Mode Decomposition (EMD) /
- order statistics filter /
- morphological filter /
- intrinsic mode image /
- image processing

HTML全文

图 1 一维正弦信号平均极值距离的计算

Figure 1. Computation of average extremum distance for a 1D sinusoidal signal

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图 2 本文实验的RGB彩色图像

Figure 2. RGB color images used in the experiments of this paper

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图 3 本文方法的分解结果

Figure 3. Decomposition results of proposed method

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图 4 Building图像分解比较

Figure 4. Decomposition comparison of Building image

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图 5 Horse图像分解比较

Figure 5. Decomposition comparison of Horse image

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图 6 图 2中图像分解量化比较

Figure 6. Decomposition quantitative comparison of images in Fig. 2

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图 7 Potted plant图像融合比较

Figure 7. Comparison of Potted plant image fusion

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图 8 Clock图像融合比较

Figure 8. Comparison of Clock image fusion

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图 9 Potted plant图像与Clock图像融合量化比较

Figure 9. Fusion quantitative comparisons of Potted plant image and Clock image

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图 10 不同攻击下Flower图像提取水印比较

Figure 10. Comparison of watermark extraction for Flower image under different attacks

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图 11 不同攻击下Lena图像提取水印比较

Figure 11. Comparison of watermark extraction for Lena image under different attacks

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表 1 不同多通道图像EMD方法生成第1个IMF图像所用时间

Table 1. Time comparison in generating the first IMF image among different EMD methods for multi-channel images

图像名称	尺寸/像素	极大值点数目	极小值点数目	时间/s
图像名称	尺寸/像素	极大值点数目	极小值点数目	SMEMD	CBEMD	本文方法
Monkey	500×480	53 622	53 602	167.13	2.00	0.11
Flower	512×480	33 037	32 502	167.06	1.19	0.09
Lena	512×512	120 846	109 962	177.45	3.65	0.15
Horse	600×450	58 088	104 171	180.35	2.79	0.12
Building	816×616	53 731	52 584	436.50	2.24	0.21

下载: 导出CSV

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