A deep learning-based convolutional neural network has been applied to denoise atomic-resolution in situ transmission electron microscopy (TEM) image datasets of catalyst nanoparticles acquired on high speed, direct electron counting detectors, where the signal is severely limited by shot noise. We leverage multislice TEM image simulation to generate a large and flexible dataset for training and testing the network, and then we apply it to real experimental images of model catalyst consisting of CeO2-supported Pt nanoparticles.

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