摘要翻译：
结合多种快速图像采集以减轻扫描噪声和漂移伪影已被证明是微米级精度和原子分辨率扫描透射电子显微镜(STEM)数据定量分析的关键。对于非常低的信噪比(SNR)图像堆--经常需要在液氮温度下进行不失真成像--图像配准特别微妙，标准方法要么失败，要么产生微妙的似是而非的重建晶格图像。我们提出了一种有效地对图像堆栈进行寄存器和平均的方法，这些堆栈由于信噪比低和容易出现单元不对齐而具有挑战性。在多图像堆栈中注册所有可能的图像对会导致显著的信息过剩。结合舞台漂移的简单物理图像，这使得能够识别不正确的图像配准，并从完整的相对移位集合中确定最佳的图像移位。我们在实验的低温干数据集上证明了我们的方法的有效性，突出了低信噪比点阵图像特有的细微伪影以及如何避免它们。在300kV温度下，样品冷却到接近液氮温度时，获得了高信噪比的平均图像，信息传输到0.72A。
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英文标题：
《Image registration of low signal-to-noise cryo-STEM data》
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作者：
Benjamin H. Savitzky, Ismail El Baggari, Colin Clement, Emily Waite,
  John P. Sheckelton, Christopher Pasco, Alemayehu S. Admasu, Jaewook Kim,
  Sang-Wook Cheong, Tyrel M. McQueen, Robert Hovden, and Lena F. Kourkoutis
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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英文摘要：
  Combining multiple fast image acquisitions to mitigate scan noise and drift artifacts has proven essential for picometer precision, quantitative analysis of atomic resolution scanning transmission electron microscopy (STEM) data. For very low signal-to-noise ratio (SNR) image stacks - frequently required for undistorted imaging at liquid nitrogen temperatures - image registration is particularly delicate, and standard approaches may either fail, or produce subtly specious reconstructed lattice images. We present an approach which effectively registers and averages image stacks which are challenging due to their low-SNR and propensity for unit cell misalignments. Registering all possible image pairs in a multi-image stack leads to significant information surplus. In combination with a simple physical picture of stage drift, this enables identification of incorrect image registrations, and determination of the optimal image shifts from the complete set of relative shifts. We demonstrate the effectiveness of our approach on experimental, cryogenic STEM datasets, highlighting subtle artifacts endemic to low-SNR lattice images and how they can be avoided. High-SNR average images with information transfer out to 0.72 A are achieved at 300 kV and with the sample cooled to near liquid nitrogen temperature.
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PDF链接：
https://arxiv.org/pdf/1710.09281