Deep learning for sub-angstrom-resolution imaging in uncorrected scanning transmission electron microscopy.

ABSTRACT: Achieving sub-ångström resolution has long been restricted to sophisticated aberration-corrected scanning transmission electron microscopy (AC-STEM). Recent advances in computational super-resolution techniques, such as deconvolution and electron ptychography, have enabled uncorrected STEM to achieve sub-ångström resolution without the need for delicate aberration correctors. However, these methods have strict requirements for sample thickness and thus have yet to be widely implemented. In this study, we introduce SARDiffuse-a deep-learning diffusion model designed to enhance spatial resolution and correct the noise level of uncorrected STEM images. Trained with experimental AC-STEM data, SARDiffuse has the capability to restore high-frequency information of STEM images, enabling sub-ångst

SUBMITTER: Qiu Z 

PROVIDER: S-EPMC12315106 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature