【作者】 薛中伟；

【导师】 陈立伟；

【作者基本信息】 哈尔滨工程大学 ， 信号与信息处理， 2011， 硕士

【摘要】 经验模式分解(EMD)是1998年Huang提出的一种全新的信号处理方法,它从根本上改变了传统Fourier变换和小波变换的思想,实现了信号的多尺度分解,在非线性、非平稳信号处理方面具有良好的性能。二维经验模式分解(BEMD)作为一维EMD的延伸和扩展,在二维信号处理领域已经得到了广泛的应用,由于二维信号的复杂性,BEMD方法还存在一些问题有待解决。本文对一维EMD的分解过程进行了详细的论述和介绍,分析了一维EMD中的常见问题,阐述了一维EMD的时频分析理论,给出了一维EMD方法的具体应用。本文重点介绍了BEMD的分解原理和实现过程,讨论了BEMD分解算法中几个关键步骤的实现。对8邻域比较法和形态学重构法两种图像极值点提取方法进行了比较和分析。在包络曲面拟合方面,实现了基于Delaunay三角剖分和基于径向基函数的两种插值方法。本文采用镜像延拓的方法解决BEMD分解过程中存在的边界问题。关于筛分停止条件,本文提出了一种基于包络均值阈值和极值点差值阈值的方法来对BEMD的筛分停止条件进行约束,最后实现了两种BEMD方法对Lena图像的分解。本文在改进BEMD分解算法的基础上,将其应用在图像处理方面。提出了一种基于IMF阈值函数的去噪方法,避免了传统BEMD方法强制去除高频分量使得图像边缘模糊的缺点,实验验证了该方法具有较好的效果。最后,将BEMD应用在图像边缘特征提取方面,将分解出的各层IMF分量逐层进行边缘特征提取,最后集合汇总输出图像边缘特征,与传统边缘特征提取算子进行比较,通过对比,验证了该方法的有效性。更多还原

【Abstract】 Empirical Mode Decomposition (EMD) is a new signal processing method, which has been recently introduced by Huang in 1998. It is fundamentally different from the traditional Fourier transform and Wavelet transform, realize the signal multi-scale decomposing. This method is adaptive and suitable for processing non-linear and non-stationary date. Bidimensional empirical mode decomposition(BEMD) is the extension and expansion of one-dimensional EMD, which has been widely used in processing bidimensional signal. On account of the complexity in bidimensional signal, the general BEMD method has some defects which need to be resolved.In this thesis, the decomposition process and some key characteristics of one-dimensional EMD is discussed in details, describe the Time-spectrum analysis theory of one-dimensional EMD, and give a specific application of EMD in one-dimension signal.This thesis is mainly introduced the principle and the decompose steps of BEMD, and discuss the implement of several key algorithms in BEMD framework. Analyze and compare the two detect regional extrema methods, the 8-neighborhood comparison method and the mathematical morphology. The upper and lower envelopes of the original image are based on two methods, which are delaunay triangulation and radial basis function interpolation, the boundary effect is solved by mirror symmetry. put forward a new condition to end the sifting process, propose two thresholds based on the envelope mean and the extreme point difference to restrain IMF decomposing process. At last, using two BEMD methods to decompose Lena image.Using the improved BEMD algorithm to process image. A noise removing method based on IMF threshold function is proposed, it avoids the shortcomings of blurred edges in using traditional BEMD method, Experiments show that the method has better results. At last, the BEMD is applied in image edge detection. Detecting the edge feature form all of the IMF, then output the final summary of image edge feature set, Experiments verify the validity of the method.更多还原