Super-resolving microscopy images of Li-ion electrodes for fine-feature quantification using generative adversarial networks  

作　　者：Orkun Furat Donal P.Finegan Zhenzhen Yang Tom Kirstein Kandler Smith Volker Schmidt 

机构地区：[1]Institute of Stochastics,Ulm University,Helmholtzstraße 18,89069,Ulm,Germany [2]National Renewable Energy Laboratory,15013 Denver W Parkway,Golden,CO,80401,USA [3]Chemical Sciences and Engineering Division,Argonne National Laboratory,9700 S.Cass Avenue,Lemont,IL,60439,USA

出　　处：《npj Computational Materials》2022年第1期650-660,共11页计算材料学（英文）

摘　　要：For a deeper understanding of the functional behavior of energy materials,it is necessary to investigate their microstructure,e.g.,via imaging techniques like scanning electron microscopy (SEM).However,active materials are often heterogeneous,necessitating quantification of features over large volumes to achieve representativity which often requires reduced resolution for large fields of view.Cracks within Li-ion electrode particles are an example of fine features,representative quantification of which requires large volumes of tens of particles.To overcome the trade-off between the imaged volume of the material and the resolution achieved,we deploy generative adversarial networks (GAN),namely SRGANs,to super-resolve SEM images of cracked cathode materials.A quantitative analysis indicates that SRGANs outperform various other networks for crack detection within aged cathode particles.This makes GANs viable for performing super-resolution on microscopy images for mitigating the trade-off between resolution and field of view,thus enabling representative quantification of fine features.

关 键 词：microstructure CRACK NETWORKS 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP391.41[自动化与计算机技术—控制科学与工程]