Advanced electron microscopy techniques,including scanning electron microscopes(SEM),scanning transmission electron microscopes(STEM),and transmission electron microscopes(TEM),have revolutionized imaging capabilities...Advanced electron microscopy techniques,including scanning electron microscopes(SEM),scanning transmission electron microscopes(STEM),and transmission electron microscopes(TEM),have revolutionized imaging capabilities.However,achieving high-quality experimental images remains a challenge due to various distortions stemming from the instrumentation and external factors.These distortions,introduced at different stages of imaging,hinder the extraction of reliable quantitative insights.In this paper,we will discuss the main sources of distortion in TEM and S(T)EM images,develop models to describe them,and propose a method to correct these distortions using a convolutional neural network.We validate the effectiveness of our method on a range of simulated and experimental images,demonstrating its ability to significantly enhance the signal-to-noise ratio.This improvement leads to a more reliable extraction of quantitative structural information from the images.In summary,our findings offer a robust framework to enhance the quality of electron microscopy images,which in turn supports progress in structural analysis and quantification in materials science and biology.展开更多
基金supported by the European Research Council(Grant 770887 PICOMETRICS to S.V.A.)The authors acknowledge financial support from the Research Foundation Flanders(FWO,Belgium)through project fundings(G034621N,G0A7723N and EOS 40007495)funding from the University of Antwerp Research Fund(BOF).
文摘Advanced electron microscopy techniques,including scanning electron microscopes(SEM),scanning transmission electron microscopes(STEM),and transmission electron microscopes(TEM),have revolutionized imaging capabilities.However,achieving high-quality experimental images remains a challenge due to various distortions stemming from the instrumentation and external factors.These distortions,introduced at different stages of imaging,hinder the extraction of reliable quantitative insights.In this paper,we will discuss the main sources of distortion in TEM and S(T)EM images,develop models to describe them,and propose a method to correct these distortions using a convolutional neural network.We validate the effectiveness of our method on a range of simulated and experimental images,demonstrating its ability to significantly enhance the signal-to-noise ratio.This improvement leads to a more reliable extraction of quantitative structural information from the images.In summary,our findings offer a robust framework to enhance the quality of electron microscopy images,which in turn supports progress in structural analysis and quantification in materials science and biology.
