Developing and Evaluating Deep Neural Network-Based Denoising for Nanoparticle TEM Images with Ultra-Low Signal-to-Noise

Joshua L. Vincent, Ramon Manzorro, Sreyas Mohan, Binh Tang, Dev Y. Sheth, Eero P. Simoncelli, David S. Matteson, Carlos Fernandez-Granda, Peter A. Crozier

Research output: Contribution to journalArticlepeer-review


A 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. 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 the network. The proposed network outperforms state-of-the-art denoising methods on both simulated and experimental test data. 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. Through a gradient-based analysis, we investigate the mechanisms learned by the network to denoise experimental images. 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. 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.

Original languageEnglish (US)
Pages (from-to)1431-1447
Number of pages17
JournalMicroscopy and Microanalysis
Issue number6
StatePublished - Dec 17 2021


  • TEM
  • convolutional neural network
  • deep learning
  • denoising
  • nanoparticles
  • noisy

ASJC Scopus subject areas

  • Instrumentation


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