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相关概念视频

Phase Contrast and Differential Interference Contrast Microscopy01:26

Phase Contrast and Differential Interference Contrast Microscopy

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Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
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X-ray Diffraction of Biological Samples01:10

X-ray Diffraction of Biological Samples

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X-ray diffraction or XRD is an analytical tool that utilizes X-rays to study ordered structures such as crystalline organic and inorganic samples, polycrystalline materials, proteins, carbohydrates, and drugs.
According to Bragg's law, when X-rays strike the sample positioned on a stage, the rays are  scattered by the electron clouds around the sample atoms. The  X-ray diffraction or scattering is caused by constructive interference of the X-ray waves that reflect off the internal...
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X-ray Crystallography02:18

X-ray Crystallography

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The size of the unit cell and the arrangement of atoms in a crystal may be determined from measurements of the diffraction of X-rays by the crystal, termed X-ray crystallography.
Diffraction
Diffraction is the change in the direction of travel experienced by an electromagnetic wave when it encounters a physical barrier whose dimensions are comparable to those of the wavelength of the light. X-rays are electromagnetic radiation with wavelengths about as long as the distance between neighboring...
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相关实验视频

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Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
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基于深度学习的动态散射介质的连贯衍射成像.

Yu Liu, Guiqin Hu, Xiuxiang Chu

    Optics express
    |January 5, 2024
    PubMed
    概括

    本研究介绍了一种深度学习方法,用于通过使用ptychographic代引擎 (PIE) 的动态散射介质进行无镜头成像. 该方法成功地重建了图像,在具有挑战性的环境中增强了PIE应用.

    科学领域:

    • 光学和光子学 在光学和光子学.
    • 计算成像技术的成像
    • 人工智能的人工智能

    背景情况:

    • 无镜头连贯衍射成像使用图形图形代引擎 (PIE) 是有效的,但与动态散射介质扎.
    • 在大气污染,海水检测和医学成像方面的应用受到分散干扰的阻碍.

    研究的目的:

    • 通过动态散射介质开发一种新的深度学习方法,用于在没有镜头的连贯衍射成像中进行图像重建.
    • 为了使PIE在以前不适合该技术的复杂环境中进行成像.

    主要方法:

    • 一个计算深度学习模型被开发用于动态散射介质图像重建.
    • 在各种分散度下评估了神经网络的有效性.
    • 使用不同散射度的混合训练评估了模型的概括能力.

    主要成果:

    • 拟议的深度学习方法成功实现了PIE无镜头成像,尽管存在非静态散射介质干扰.
    • 神经网络在不同的散射条件下证明了PIE图像恢复的有效性.
    • 实验结果证实了模型对不同散射水平的概括能力.

    结论:

    • 新的深度学习方法克服了PIE在动态分散介质中的局限性.

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  • 这种方法扩大了PIE在复杂环境中的实际应用.
  • 该技术为大气,海洋和生物医学成像提供了巨大的潜力.