论文标题
根据毛孔的形态及其空间分布的嘈杂的多孔材料的嘈杂微视材料图像中代表性体积元素识别的强大技术
Robust Technique for Representative Volume Element Identification in Noisy Microtomography Images of Porous Materials Based on Pores Morphology and Their Spatial Distribution
论文作者
论文摘要
微传输是一种强大的材料研究方法。它使得在研究中有用的多孔培养基的物理特性可获得多孔介质的物理特性。应用方法之一是计算金属陶瓷（CERMET）膜的孔隙率，孔径，表面积和其他参数，这些膜在过滤工业中广泛传播。微观学方法是有效的，因为与常规技术相比，所有这些参数都是同时计算的。然而，Micro-CT重建图像上的计算似乎很耗时，因此应选择代表性的体积元素以加快它们的速度。这项研究阐明了代表性的基本体积识别，而没有考虑任何物理参数，例如孔隙率等。因此，即使在噪声和灰度图像中也可以找到体积元素。提出的方法是灵活的，并且在各向异性样品的情况下不会高估体积大小。如果对图像进行过滤和二进制，则可以将获得的音量元素用于计算域的物理特性，或者用于选择最佳过滤参数以进行剥离过程。
Microtomography is a powerful method of materials investigation. It enables to obtain physical properties of porous media non-destructively that is useful in studies. One of the application ways is a calculation of porosity, pore sizes, surface area, and other parameters of metal-ceramic (cermet) membranes which are widely spread in the filtration industry. The microtomography approach is efficient because all of those parameters are calculated simultaneously in contrast to the conventional techniques. Nevertheless, the calculations on Micro-CT reconstructed images appear to be time-consuming, consequently representative volume element should be chosen to speed them up. This research sheds light on representative elementary volume identification without consideration of any physical parameters such as porosity, etc. Thus, the volume element could be found even in noised and grayscale images. The proposed method is flexible and does not overestimate the volume size in the case of anisotropic samples. The obtained volume element could be used for computations of the domain's physical characteristics if the image is filtered and binarized, or for selections of optimal filtering parameters for denoising procedure.
