利用波束倾斜测量和环境细胞观测中的深度学习降低膜源噪声。

IF 1.9
Fumiaki Ichihashi, Yoshio Takahashi, Toshiaki Tanigaki
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引用次数: 0

摘要

使用环境电池的电子显微镜是观察气体和液体中的催化剂和其他纳米材料的有力工具。环境电池必须包含非晶氮化硅膜,因为它们保护样品环境免受电子显微镜的真空影响,并使电子束能够通过电池。然而,薄膜在投影图像上叠加不均匀的对比度,降低图像质量。我们提出了一种使用Noise2Noise(一种深度学习方法)去除来自膜的噪声的方法,用于一系列具有轻微电子束倾斜的透射电子显微镜图像,并评估了其有效性。我们成功地去除了膜源噪声,同时保留了细胞中样品的信息。我们还成功地有效地去除了泊松噪声。我们相信这种方法将使需要高信噪比的测量,以前只能在真空中观察到,在环境电池中进行。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reduction of Membrane-derived Noise Using Beam-tilt Measurement and Deep Learning in Observation using Environmental Cell.

Electron microscopy using an environmental cell is a powerful tool for observing catalysts and other nanomaterials in gases and liquids. An environmental cell must contain amorphous silicon-nitride membranes because they protect the sample environment from the vacuum of the electron microscope and enable the electron beam to pass through the cell. However, the membranes superimpose non-uniform contrast on the projected image, degrading image quality. We propose a method for removing the noise derived from the membranes using Noise2Noise, a deep-learning method, for a series of transmission-electron-microscope images with slight electron-beam tilt and evaluated its effectiveness. We succeeded in removing the membrane-derived noise while retaining the information of the sample in the cell. We also succeeded in efficiently removing Poisson noise. We believe this method will enable measurements requiring high signal-to-noise ratios, which could previously only be observed in a vacuum, to be conducted in an environmental cell.

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