  A deep convolutional neural network has been developed to denoise
atomic-resolution TEM image datasets of nanoparticles acquired using direct
electron counting detectors, for applications where the image signal is
severely limited by shot noise. The network was applied to a model system of
CeO2-supported Pt nanoparticles. We leverage multislice image simulations to
generate a large and flexible dataset for training and testing the network. The
proposed network outperforms state-of-the-art denoising methods by a
significant margin both on simulated and experimental test data. Factors
contributing to the performance are identified, including most importantly (a)
the geometry of the images used during training and (b) the size of the
network's receptive field. Through a gradient-based analysis, we investigate
the mechanisms learned by the network to denoise experimental images. This
shows that the network exploits global and local information in the noisy
measurements, for example, by adapting its filtering approach when it
encounters atomic-level defects at the nanoparticle surface. Extensive analysis
has been done to characterize the network's ability to correctly predict the
exact atomic structure at the nanoparticle surface. Finally, we develop an
approach based on the log-likelihood ratio test that provides a quantitative
measure of the agreement between the noisy observation and the atomic-level
structure in the network-denoised image.
