Denoising is a key issue for quantitative high-resolution transmission electron microscopy (HRTEM) and its roles become more critical for applications in beam-sensitive materials and dynamic characterizations where the attainable signal-to-noise ratio (SNR) of HRTEM images is often limited. In this article, we introduce a novel nonlinear filter 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 frequently used Winer filter that was performed for each individual image, this novel filter, denoted as 3DSF, exhibits higher SNR, less artifacts, and more computation efficiency, which works particularly well for TEM images comprising of periodic information and feature similarities in sequent micrographs within the 3D data cube. Application of this novel 3DSF is further demonstrated in a few examples that includes to capture the defect dynamics in graphene and elegant structure of MOFs.
Bibliographical noteKAUST Repository Item: Exported on 2022-05-26
Acknowledgements: F. Lin acknowledges financial support from the National Natural Science Foundation of China (Grant Nos. 61971201 and 61571197) and from the Science and Technology Planning Project of Guangzhou (No. 2021-02-08-06-3007-0009).
ASJC Scopus subject areas
- Atomic and Molecular Physics, and Optics
- Electronic, Optical and Magnetic Materials