Reconstruction of partially sampled multi-band images - Application to STEM-EELS imaging

TL;DR

This paper introduces two novel algorithms for reconstructing partially sampled multi-band STEM-EELS images, leveraging spectral and spatial properties, to enable high-quality data recovery with reduced electron beam exposure.

Contribution

It presents new reconstruction algorithms that exploit spectral and spatial structures for STEM-EELS data, improving image recovery from partial scans.

Findings

Algorithms outperform traditional methods on phantom datasets

Effective in reconstructing real EELS spectrum-images

Reduce electron dose while maintaining image quality

Abstract

Electron microscopy has shown to be a very powerful tool to map the chemical nature of samples at various scales down to atomic resolution. However, many samples can not be analyzed with an acceptable signal-to-noise ratio because of the radiation damage induced by the electron beam. This is particularly crucial for electron energy loss spectroscopy (EELS) which acquires spectral-spatial data and requires high beam intensity. Since scanning transmission electron microscopes (STEM) are able to acquire data cubes by scanning the electron probe over the sample and recording a spectrum for each spatial position, it is possible to design the scan pattern and to sample only specific pixels. As a consequence, partial acquisition schemes are now conceivable, provided a reconstruction of the full data cube is conducted as a post-processing step. This paper proposes two reconstruction algorithms for multi-band images acquired by STEM-EELS which exploits the spectral structure and the spatial smoothness of the image. The performance of the proposed schemes is illustrated thanks to experiments conducted on a realistic phantom dataset as well as real EELS spectrum-images.