The potential for bayesian compressive sensing to significantly reduce electron dose in high-resolution STEM images

The use of high-resolution imaging methods in scanning transmission electron microscopy (STEM) is limited in many cases by the sensitivity of the sample to the beam and the onset of electron beam damage (for example, in the study of organic systems, in tomography and during in situ experiments). To demonstrate that alternative strategies for image acquisition can help alleviate this beam damage issue, here we apply compressive sensing via Bayesian dictionary learning to high-resolution STEM images. These computational algorithms have been applied to a set of images with a reduced number of sampled pixels in the image. For a reduction in the number of pixels down to 5% of the original image, the algorithms can recover the original image from the reduced data set. We show that this approach is valid for both atomic-resolution images and nanometer-resolution studies, such as those that might be used in tomography datasets, by applying the method to images of strontium titanate and zeolites. As STEM images aren the electron optics or the alignment of the microscope itself.