Superresolution of multiscale images from materials sciences using geometrical features

使用几何特征的材料科学多尺度图像的超分辨率

• 批准号: 406582924
• 负责人: Professorin Dr. Claudia Redenbach
• 金额: --
• 依托单位: Institut de Chimie de la Matière Condensée de Bordeaux (UMR 5026)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/406582924?language=en
• 关键词: Superresolution; multiscale; images; materials; sciences

Recent and ongoing developments in imaging techniques and computational analysis deeply modify the way materials sciences and engineering consider their objects of research. Our project will contribute to this direction of research by developing new superresolution methods guided by high-resolution local subimages of 3D materials data.The mathematical methods of choice will be based on local and global Generalized Gaussian Mixture Models as well as Student-t Mixture Models in conjunction with variational methods. Appropriate geometrical features related to the engineering topics have to be established to provide an evaluation platform for the superresolution images, and to be directly involved into the Bayesian and variational models. The mathematical models will be developed, analyzed and appropriate efficient algorithms will be derived, including an examination of their convergence behavior. The models will be extended to multimodal images, where due to the size of the structures of interest, the high resolution image are taken by serial sectioning (FIB-SEM) tomography and the low resolution images by micro computed tomography. This requires to take the special acquisition of FIB-SEM tomographic images, in particular curtaining effects and the anisotropy with respect to the third dimension into account.A numerical evaluation of the relevance and the benefit of the developed superresolution methods will be performed by comparing the effective properties computed for reactive flow in porous media.

成像技术和计算分析的最新和持续发展深刻地改变了材料科学和工程考虑其研究对象的方式。我们的项目将为这一研究方向做出贡献，通过开发新的超分辨率方法来指导3D材料数据的高分辨率局部子图像。选择的数学方法将基于局部和全局广义高斯混合模型以及学生t混合模型与变分方法。建立与工程主题相关的几何特征，为超分辨率图像提供评价平台，并直接引入贝叶斯模型和变分模型。数学模型将被开发，分析和适当的有效算法将被导出，包括检查其收敛行为。该模型将被扩展到多模态图像，其中由于感兴趣的结构的大小，高分辨率的图像是由连续切片（FIB-SEM）断层扫描和低分辨率的图像由微型计算机断层扫描。这就需要考虑特殊的FIB-SEM断层图像采集，特别是帘幕效应和三维各向异性的影响。通过比较多孔介质中反应流的有效特性，对所开发的超分辨率方法的相关性和优势进行数值评估。