Abstract

High-resolution electron microscopy is achievable only when a high electron dose is employed, a practice that may cause damage to the specimen and, in general, affects the observation. This drawback sets some limitations on the range of applications of high-resolution electron microscopy. Our work proposes a strategy, based on machine learning, which enables a significant improvement in the quality of Scanning Transmission Electron Microscope images generated at low electron dose, strongly affected by Poisson noise. In particular, we develop an autoencoder, trained on a large database of images, which is thoroughly tested on both synthetic and actual microscopy data. The algorithm is demonstrated to drastically reduce the noise level and approach ground-truth precision over a broad range of electron beam intensities. Importantly, it does not require human data pre-processing or the explicit knowledge of the dose level employed and can run at a speed compatible with live data acquisition. Furthermore, a quantitative unbiased benchmarking protocol is proposed to compare different denoising workflows.

In the scanning transmission electron microscope, fast-scanning and frame-averaging are two widely used approaches for reducing electron-beam damage and increasing image signal noise ratio which require no additional specialized hardware. Unfortunately, for scans with short pixel dwell-times (less than 5 μs), line flyback time represents an increasingly wasteful overhead. Although beam exposure during flyback causes damage while yielding no useful information, scan coil hysteresis means that eliminating it entirely leads to unacceptably distorted images. In this work, we reduce this flyback to an absolute minimum by calibrating and correcting for this hysteresis in postprocessing. Substantial improvements in dose efficiency can be realized (up to 20%), while crystallographic and spatial fidelity is maintained for displacement/strain measurement.