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VARIABLE SAMPLING OF THE EMPIRICAL MODE DECOMPOSITION OF TWO-DIMENSIONAL SIGNALS

机译:二维信号的经验模态分解的可变采样

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摘要

Previous work on empirical mode decomposition in two dimensions typically generates a residue with many extrema points. In this paper we propose an improved method to decompose an image into a number of intrinsic mode functions and a residue image with a minimum number of extrema points. We further propose a method for the variable sampling of the two-dimensional empirical mode decomposition. Since traditional frequency concept is not applicable in this work, we introduce the concept of empiquency, shortform for empirical mode frequency, to describe the signal oscillations. The very special properties of the intrinsic mode functions are used for variable sampling in order to reduce the number of parameters to represent the image. This is done blockwise using the occurrence of extrema points of the intrinsic mode function to steer the sampling rate of the block. A method of using overlapping 7×7 blocks is introduced to overcome blocking artifacts and to further reduce the number of parameters required to represent the image. The results presented here shows that an image can be successfully decomposed into a number of intrinsic mode functions and a residue image with a minimum number of extrema points. The results also show that subsampling offers a way to keep the total number of samples generated by empirical mode decomposition approximately equal to the number of pixels of the original image.
机译:先前关于二维经验模式分解的工作通常会生成具有许多极值点的残差。在本文中,我们提出了一种改进的方法,可以将图像分解为多个固有模式函数和具有最少极值点的残差图像。我们进一步提出了一种用于二维经验模式分解的变量采样的方法。由于传统的频率概念不适用于这项工作,因此我们引入了经验性的概念,即经验模态频率的缩写,来描述信号的振荡。本征模式函数的非常特殊的属性用于变量采样,以减少表示图像的参数数量。这是通过使用固有模式函数的极值点的出现来逐块完成的,以控制块的采样率。引入了使用重叠的7×7块的方法来克服块状伪影并进一步减少表示图像所需的参数数量。此处显示的结果表明,图像可以成功地分解为许多固有模式函数和具有最少极值点的残差图像。结果还表明,二次采样提供了一种方法,可以使通过经验模式分解生成的样本总数保持近似等于原始图像的像素数。

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