Preserving spatial and energy resolution in multi-dimensional hyperspectral image datasets via rigid image registration.

Monochromated electron energy loss spectroscopy in advanced scanning transmission electron microscopes has become a powerful tool for probing local electronic and vibrational spectra in many materials. However, acquiring high-quality multi-dimensional hyperspectral image datasets remains challenging due to sample drift, low signal-to-noise ratio (SNR), and detector artifacts. These limitations often require shorter acquisition times to reduce their influence, which can in turn compromise the spatial and energy resolution of the final results. To overcome these challenges, we develop a new approach that aligns and integrates multi-frame, fast-scan datasets using optimized image-registration algorithms and selection criteria to achieve high-SNR, low-drift results. By mitigating time-dependent experimental instabilities, this approach effectively preserves the intrinsic energy and spatial resolution of the instrument in the final integrated dataset. Ultimately, this provides an efficient and versatile strategy for capturing high-fidelity multi-dimensional hyperspectral images without compromising the attainable resolution.