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发展和正在进行的发展深刻地修改了材料科学和工程的方式考虑其研究对象。我们的项目将通过开发以3D材料数据的高分辨率局部子图为指导的新的超分辨率方法来促进这一研究的方向。选择的数学方法将基于局部和全球通用高斯混合模型以及与变量方法结合的学生-T混合模型。必须建立与工程主题相关的适当几何特征，以为超分辨率图像提供评估平台，并直接参与贝叶斯和变异模型。将得出数学模型，分析并得出适当的有效算法，包括检查其收敛行为。这些模型将扩展到多模式图像，由于感兴趣的结构的大小，高分辨率图像是通过串行切片（FIB-SEM）层析成像（FIB-SEM）层析成像拍摄的，并且通过微型计算机断层扫描量表进行低分辨率图像。这需要对FIB-SEM层析成像图像进行特别获取，尤其是考虑到第三维的窗帘效应，以及对第三维的各向异性。对相关性的数值评估和所开发的超分辨率方法的好处将通过比较在多孔介质中计算的有效属性来执行。