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Auto-encoders for Noise Reduction in Scanning Transmission Electron Microscopy

In recent years, machine learning has been applied to a wide variety of fields with increasing success. With the current availability of cheap computing power and the advent of deep learning, the applications of several powerful neural network architectures, such as convolutional neural networks, recurrent neural networks and auto-encoding neural networks, have shown remarkable accuracy in classifying various data sets [1]. In particular, the auto-encoder (AE) architecture excels at recovering signal from noisy, or corrupted input data [2]. In the scanning transmission electron microscope (STEM), images are often plagued by various sources of noise. This is especially problematic for imaging beam sensitive materials where the scan speed is exceptionally fast and/or the beam current is very low [3]. Here, we investigate the applicability of using AEs for noise reduction in STEM images.