Enhanced EDX images by fusion of multimodal SEM images using pansharpening techniques

Summary The goal of this paper is to explore the potential interest of image fusion in the context of multimodal scanning electron microscope (SEM) imaging. In particular, we aim at merging the backscattered electron images that usually have a high spatial resolution but do not provide enough discriminative information to physically classify the nature of the sample, with energy‐dispersive X‐ray spectroscopy (EDX) images that have discriminative information but a lower spatial resolution. The produced images are named enhanced EDX. To achieve this goal, we have compared the results obtained with classical pansharpening techniques for image fusion with an original approach tailored for multimodal SEM fusion of information. Quantitative assessment is obtained by means of two SEM images and a simulated dataset produced by a software based on PENELOPE. Lay description Scanning Electron Microscopy (SEM) allows us to acquire images from a given sample using different modalities. These different modalities bring complementary information, for example topological information, elemental composition information, or local mean atomic number information. In this article, we work with SEM backscattered electrons (BSE), which provides high spatial resolution information but are not accurate in the chemical composition. In addition, we also work with Energy Dispersive X‐ray Spectrometry (EDX) images, which have accurate chemical composition information, but have low spatial resolution. The purpose of these studies is to analyze the interest of merging this information to improve the multimodal SEM images acquisition. To perform this study, we have implemented various image fusion techniques and have compared them to a new method we have proposed. Most of the techniques implemented are tested on various real datasets and on one synthetic dataset, which gathers different major artifacts present in the fusion of information. It happens that according to accuracy, consistency, and consistency of gradient criteria, our method obtains a better result. Moreover, our technique has also better visual results. Then, the experiment result in our article shows that the proposed method is able to increase the resolution of the EDX images.