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首页> 外文期刊>Applied optics >TWO-DIMENSIONAL WAVELET TRANSFORM AND APPLICATION TO HOLOGRAPHIC PARTICLE VELOCIMETRY
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TWO-DIMENSIONAL WAVELET TRANSFORM AND APPLICATION TO HOLOGRAPHIC PARTICLE VELOCIMETRY

机译:二维小波变换及其在全息颗粒速度分析中的应用

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摘要

The goal of holographic particle velocimetry is to infer fluid velocity patterns from images reconstructed from doubly exposed holograms of fluid volumes seeded with small particles. The advantages offered by in-line holography in this context usually make it the method of choice, but seeding densities sufficient to achieve high spatial resolution in the sampling of the velocity fields cause serious degradation, through speckle, of the signal-to-noise ratio in the reconstructed images. The in-line method also leads to a great depth of field in paraxial viewing of reconstructed images, making it essentially impossible to estimate particle depth with useful accuracy. We present here an analysis showing that these limitations can be circumvented by Variably scaled correlation, or wavelet transformation. The shift variables of the wavelet transform are provided automatically by the optical correlation methodology. The variable scaling of the wavelet transform derives, in this case, directly from the need to accommodate varying particle depths. To provide such scaling, we use a special optical system incorporating prescribed variability in spacings and focal length of lenses to scan through the range of particle depths. Calculation shows, among other benefits, improvement by approximately two orders of magnitude in depth resolution. A much higher signal-to-noise ratio together with faster data extraction and processing should be attainable. [References: 22]
机译:全息粒子测速仪的目标是从由小粒子注入的流体体积的双曝光全息图重建的图像中推断出流体速度模式。在线全息术在这种情况下提供的优势通常使其成为选择的方法,但是足以在速度场采样中实现高空间分辨率的播种密度会导致通过斑点的信噪比严重降低在重建的图像中。在线方法还导致在旁轴观看重建图像时产生很大的景深,从而使得根本不可能以有用的精度估算粒子深度。我们在这里进行分析表明,可以通过可变比例相关或小波变换来规避这些限制。小波变换的移位变量由光学相关方法自动提供。在这种情况下,小波变换的可变缩放比例直接来自于适应变化的粒子深度的需要。为了提供这样的缩放比例,我们使用了一种特殊的光学系统,该系统结合了规定的镜头间距和焦距可变性来扫描整个粒子深度范围。计算显示,除其他好处外,深度分辨率提高了大约两个数量级。应该获得更高的信噪比以及更快的数据提取和处理。 [参考:22]

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