Fig. 1: Schematic illustration of the experimental setup and acquired data.

a An overview of the modified UNet architecture used for Noise2Void denoising where the inputs are noisy dual-channel (HAADF & BF) STEM video frames (left) and the outputs are the same frames denoised (right). ‘Conv2d’, ‘ConvTranspose2d’ and ‘LeakyReLU’ refer to convolutional blocks, transpose-convolution blocks and leaky ‘ReLU’ activation blocks, respectively, while ‘Concat’ and ‘AvgBlurPool’ refer to channel-wise concatenation and bilinear downsampling³⁸, respectively. The black horizontal arrows represent UNet skip connections. b The results of a separate atom-finding step applied to the example image above. In the denoised output, 89% more adatom features are identified compared to the raw image, showing the improved performance of atom finding analysis when Noise2Void is used in a data-analysis pipeline. Inset are corresponding atomic diagrams of the magnified region, showing the graphene lattice positions with gold adatoms overlain. The blue arrows highlight gold atoms that were only detected after Noise2Void denoising was applied. Also shown is a model of the TEM liquid-cell, showing the convergent electron probe (green), the few-layer graphene windows (black) and the boron nitride spacer layer (blue).