Alsari, Ali, Zhang, Jingqiong ORCID: https://orcid.org/0000-0003-2980-8145, Farr, Nicholas T.H, Rodenburg, Cornelia and Mihaylova, Lyudmila (2026) A spectral-spatial deep learning for secondary electron hyperspectral image super-resolution. In: 29th International Conference on Information Fusion (FUSION). Institute of Electrical and Electronics Engineers (IEEE), Piscataway, New Jersey. (In Press)

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Abstract

The secondary electron hyperspectral imaging (SEHI) is a promising surface analysis method, especially for nano/micro chemical materials. However, SEHI poses challenges for achieving a trade-off between low-resolution imaging over a large field of view and high-resolution imaging over a limited field of view, and avoiding potential sample alteration caused by electron beam exposure. To address this limitation, we introduce, for the first time, a spectral-spatial deep learning model (SSDL) that produces a super-resolution SEHI image from a low resolution SEHI image, while substantially reducing the risk of sample alteration. Specifically, the SSDL model incorporates a spectral squeeze-and-excitation (SE) module and a spectral spatial fusion block, enhancing its flexibility in extracting both spatial and spectral features. Further, inception-residual modules are embedded within the proposed SSDL to capture multi-scale structural features. A custom spatial–spectral loss function is introduced, integrating a mean squared error (MSE), a spatial gradient loss, and a spectral angle mapper loss, which are essential for simultaneously preserving spectral and spatial integrity. The proposed model shows high performance compared to state-of-the-art methods during testing on two real SEHI datasets. Ablation experiments further confirm the effectiveness of the spectral SE block, inception-residual modules, and the custom loss function. This work demonstrates that deep learning can greatly enhance SEHI by considerably increasing the scanning throughput while preserving high-resolution, thereby improving the practical utility of SEHI for materials science.

Item Type:	Conference Proceedings
Title of Proceedings:	29th International Conference on Information Fusion (FUSION)
Uncontrolled Keywords:	super-resolution, secondary electron hyperspectral imaging, Deep learning
Subjects:	Q Science > Q Science (General) T Technology > T Technology (General)
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Engineering (ENG)
Related URLs:	Organisation
Last Modified:	29 May 2026 12:23
URI:	https://gala.gre.ac.uk/id/eprint/53620

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