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.Īrtificial Intelligence & Nanotechnology are promising areas for the future of humanity. Extensive analysis has been done to characterize the network's ability to correctly predict the exact atomic structure at the nanoparticle surface. This shows that the network exploits both extended and local information in the noisy measurements, for example, by adapting its filtering approach when it encounters atomic-level defects at the nanoparticle surface. Through a gradient-based analysis, we investigate the mechanisms learned by the network to denoise experimental images. Factors contributing to the performance are identified, including (a) the geometry of the images used during training and (b) the size of the network's receptive field. The proposed network outperforms state-of-the-art denoising methods on both simulated and experimental test data. We leverage multislice image simulations to generate a large and flexible dataset for training the network. The network was applied to a model system of CeO 2 -supported Pt nanoparticles. CNNs trained for segmentation have also been used to locate the position of atomic columns as well as to estimate their occupancy (Madsen et al., 2018) in relatively high SNR (S)TEM images (i.e., SNR = ∼10).Ī deep convolutional neural network has been developed to denoise atomic-resolution transmission electron microscope image datasets of nanoparticles acquired using direct electron counting detectors, for applications where the image signal is severely limited by shot noise. In electron microscopy, deep CNNs are rapidly being developed for denoising in a variety of applications, including structural biology (Buchholz et al., 2019 Bepler et al., 2020), semiconductor metrology (Chaudhary et al., 2019 Giannatou et al., 2019), and drift correction (Vasudevan & Jesse, 2019), among others (Ede & Beanland, 2019 Lee et al., 2020 Wang et al., 2020 Lin et al., 2021 Spurgeon et al., 2021), as highlighted in a recent review (Ede, 2020). Convolutional neural networks (CNNs) achieve state-of-theart denoising performance on natural images Tian et al., 2019) and are an emerging tool in various fields of scientific imaging, for example, in fluorescence light microscopy (Belthangady & Royer, 2019 Zhang et al., 2019) and in medical diagnostics (Yang et al., 2017 Jifara et al., 2019).
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