Complete Compressed Sensing Framework for STEM Tomography
Laurène Donati, EPFL STI LIB

Meeting • 19 July 2016 • BM 4 233

Abstract
A central challenge in scanning transmission electron microscopy (STEM) is to reduce the electron radiation dosage required for accurate imaging of 3D biological nano-structures. In this work, we demonstrate that random-beam scanning in STEM (RB-STEM) fulfills the "incoherence" condition required by the theory of compressed sensing when the image is expressed in terms of wavelets. We then propose a regularized tomographic reconstruction framework to recover high-quality images from RB-STEM datasets. Finally, we present a novel DigitalMicrograph® plug-in that implements stable random-beam scanning for data acquisition in STEM mode, leading to a further reduction in the heating of electron-sensitive biological samples. This complete application of compressed sensing principles to STEM paves the way for a practical implementation of RB-STEM and opens new perspectives for high-quality reconstructions in electron tomography.