Abstract
•We propose an approach to filter image series where a series of HRTEM images is stacked into a 3D data cube and then treated with Wiener filter in 3D domain with suitable fitting parameters.•Comparing to the conventional Winer filtering performed individually for single image, the 3DSF exhibits higher SNR, less artifacts, and more computation efficiency.•Subtle lattice shrinkage on 40 imaging states becomes observable and measurable directly filtered from low-dose experimental images of MOF with a total dose of ∼8 e/Å2.•3DSF is expected to become an online real-time filter and provides researchers with higher quality images for structural evolutions in dynamic process and low-dose TEM experiment.
De-noising is an important issue in quantitative high-resolution transmission electron microscopy (HRTEM), and its roles become even more important in applications such as beam-sensitive materials and dynamic characterisations, where the attainable signal-to-noise ratio (SNR) of HRTEM images is frequently limited. In this study, we introduce a three-dimensional stacked filter (3DSF), a novel non-linear filter in which a series of HRTEM images is stacked into a 3D data cube and then treated with the Wiener filter in the 3D domain. In comparison to the traditional Wiener filter, which is widely used for individual images, this filter can accurately estimate the power spectral density of noise and filter images with a higher SNR, fewer artefacts and greater computation efficiency, which works particularly well for HRTEM images containing periodic information and feature similarities in successive micrographs, as demonstrated by simulated and experimental images of graphene and metal–organic framework (MOF). When applied to an ultra-low dose (∼8 e/Å2) HRTEM image stack of MOF MIL-101, the 3DSF could distinguish 40 consecutive frames, revealing the trajectory of subtle lattice shrinkage during the exposure.