用于高数密度和非球形颗粒的散焦测深技术
Depth from Defocus Technique for High Number Densities and Non-spherical Particles
AI总结：
本研究通过引入重叠颗粒图像识别和灰度级梯度估计模糊核宽度两项改进，将散焦测深技术扩展到高数浓度和非球形颗粒的尺寸与位置测量，并利用专用实验装置验证了算法的精度与局限性。
摘要：
散焦测深（DFD）成像技术用于测量分散两相流中颗粒的尺寸和数浓度，迄今为止仅限于相对稀疏的颗粒密度，并且只能识别球形颗粒。本研究考察了对该技术的两项改进，显著拓宽了其应用范围。第一项改进引入了一种图像处理程序，能够识别并测量相互重叠的颗粒图像。这提高了在测量体积内可被识别和处理的颗粒的可容忍数浓度。第二项改进探索了在观测体积内确定非球形颗粒尺寸和位置的可能性。这两项改进都建立在近期理论工作的基础上，不仅利用模糊、离焦图像的灰度级，还利用垂直于标称颗粒或颗粒集合轮廓的灰度级梯度。该灰度级梯度用于估计模糊核的宽度，并假设该模糊核保持高斯分布。这些增强通过专用实验装置进行了实验验证，在该装置中，已知尺寸、形状和图像重叠程度的颗粒可以被系统地改变。该实验装置提供了基准数据，用于量化处理算法的精度和局限性。
英文摘要：
The Depth from Defocus (DFD) imaging technique for measuring the size and number concentration of particles in a dispersed two-phase flow has up to now been restricted to relatively sparse particle densities and to identifying only spherical particles. The present study examines two advancements to the technique, widening its range of application significantly. The first advancement introduces an image processing procedure which can identify and size particle images which are overlapping. This increases the tolerable number concentration of particles which can be identified and processed within the measurement volume. The second advancement explores the possibility of determining the size and position of non-spherical particles within an observation volume. Both advancements build on recent theoretical work, utilizing not only the gray level of the blurred, out-of-focus images, but also the gradient of the gray level normal to the nominal particle or particle ensemble contour. This gray-level gradient is used to estimate the width of the blur kernel, which is assumed to remain Gaussian. These enhancements are experimentally validated using a dedicated apparatus in which particles of known size, shape and degree of overlapping images can be systematically varied. This experimental setup provides benchmark data to quantify the accuracy and limitations of the processing algorithms.
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