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Noise reduction in digital speckle pattern interferometry using bidimensional empirical mode decomposition

机译:使用二维经验模态分解的数字散斑干涉测量中的降噪

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We propose a bidimensional empirical mode decomposition (BEMD) method to reduce speckle noise in digital speckle pattern interferometry (DSPI) fringes. The BEMD method is based on a sifting process that decomposes the DSPI fringes in a finite set of subimages represented by high and low frequency oscillations, which are named modes. The sifting process assigns the high frequency information to the first modes, so that it is possible to discriminate speckle noise from fringe information, which is contained in the remaining modes. The proposed method is a fully data-driven technique, therefore neither fixed basis functions nor operator intervention are required. The performance of the BEMD method to denoise DSPI fringes is analyzed using computer-simulated data, and the results are also compared with those obtained by means of a previously developed one-dimensional empirical mode decomposition approach. An application of the proposed BEMD method to denoise experimental fringes is also presented. (c) 2008 Optical Society of America.
机译:我们提出了一种二维经验模式分解(BEMD)方法,以减少数字散斑干涉图(DSPI)条纹中的散斑噪声。 BEMD方法基于一个筛选过程,该过程将DSPI条纹分解为由高频和低频振荡代表的有限子图像集,这些子图像被称为模式。筛选过程将高频信息分配给第一模式,从而可以将散斑噪声与包含在其余模式中的条纹信息区分开。所提出的方法是一种完全由数据驱动的技术,因此既不需要固定基础功能也不需要操作员干预。使用计算机模拟数据分析了BEMD方法对DSPI条纹进行去噪的性能,并将结果与​​通过先前开发的一维经验模式分解方法获得的结果进行了比较。还提出了所提出的BEMD方法在去除实验条纹上的应用。 (c)2008年美国眼镜学会。

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