Denoising Large In Situ TEM Image Datasets: A Convolutional Neural Network-based Approach

Heterogenous catalysts are an important class of materials that receive considerable research attention due to their large impact on energy and the environment. Aberration-corrected in situ environmental transmission electron microscopy (ETEM) is a powerful catalyst characterization tool capable of providing atomic-scale structural information from active catalysts under reaction conditions. Recent advancements in the realization of highly efficient direct electron detectors now enable atomicallyresolved ETEM images to be acquired with a temporal resolution in the millisecond or sub-millisecond regime, which is where catalytically relevant structural reconfigurations are thought to occur [1,2,3]. While there is potentially much to be gained from applying these new detectors to catalytic nanomaterials characterization, acquiring ETEM image series with high temporal resolution necessarily produces datasets that can be unwieldly in size and severely degraded by noise, rendering traditional image processing approaches impractical and ineffective at extracting useful scientific information. For example, recording a 64 megapixel in situ ETEM movie at 2.5 millisecond time resolution would produce around 100 gigabytes and many hundreds of frames of noisy image data every second. We are interested in exploring innovative approaches to image processing that leverage new advances in data science on large, noisy, atomic-resolution in situ ETEM data sets.