Developing and Evaluating Deep Neural Network-Based Denoising for Nanoparticle TEM Images with Ultra-Low Signal-to-Noise
Developing and Evaluating Deep Neural Network-Based Denoising for Nanoparticle TEM Images with Ultra-Low Signal-to-Noise
复制标题
DOI：
10.1017/s1431927621012678
复制
发表时间：
2021-01
影响因子：
2.8
通讯作者：
Joshua L. Vincent;R. Manzorro;S. Mohan;Binh Tang;D. Y. Sheth;Eero P. Simoncelli;D. Matteson;C. Fer
中科院分区：
文献类型：
--
作者：
Joshua L. Vincent;R. Manzorro;S. Mohan;Binh Tang;D. Y. Sheth;Eero P. Simoncelli;D. Matteson;C. Fer
摘要：本文开发了一种深度卷积神经网络，用于对使用直接电子计数检测器获得的纳米颗粒原子分辨率透射电子显微镜图像数据集进行降噪，用于图像信号严重受散粒噪声限制的应用。将该网络应用于ceo2负载的Pt纳米颗粒模型体系中。我们利用多片图像模拟来生成一个大而灵活的数据集来训练网络。所提出的网络在模拟和实验测试数据上都优于最先进的去噪方法。确定了影响性能的因素，包括(a)训练期间使用的图像的几何形状和(b)网络接受域的大小。通过基于梯度的分析，我们研究了网络对实验图像去噪的机制。这表明该网络利用了噪声测量中的扩展信息和局部信息，例如，当它遇到纳米颗粒表面的原子级缺陷时，通过调整其过滤方法。广泛的分析已经完成，以表征网络的能力，正确预测纳米颗粒表面的确切原子结构。最后，我们开发了一种基于对数似然比检验的方法，该方法提供了一种定量测量噪声观测与网络降噪图像中原子水平结构之间一致性的方法。
Abstract 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.