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Phase Contrast and Differential Interference Contrast Microscopy01:26

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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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基于内部转移相位编码的深度学习支持的单射边缘投影配置测量.

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    此摘要是机器生成的。

    本研究引入了一种新的深度学习方法,用于单射边缘投影特征测量 (FPP),以准确确定3D形状. 该技术增强了用于动态测量的相位和边缘顺序提取.

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    科学领域:

    • 光学计量学 在光学计量学
    • 计算机视觉 计算机视觉
    • 人工智能的人工智能

    背景情况:

    • 单射边缘投影特征测量 (FPP) 对于动态3D测量至关重要.
    • 当前的深度学习方法在与同时的包裹阶段和边缘顺序提取方面存在困难.
    • 模糊性来自于从单个图像中估计连续相位和离散边缘顺序,特别是对于孤立的物体.

    研究的目的:

    • 为准确的绝对相位恢复开发一个支持深度学习的单次FPP方法.
    • 为了解决联合阶段和边缘订单估计中固有的模糊性.
    • 为了在动态场景中实现复杂和孤立对象的强大的3D测量.

    主要方法:

    • 提出了一个物理约束的双网络架构,集成内部转移相位编码.
    • 嵌入边缘顺序在单个边缘图案的相位移组件中.
    • 采用两种轻量级卷积神经网络进行脱相位和边缘顺序回归和分类.

    主要成果:

    • 实现了高精度的绝对相位恢复,与多技术相似.
    • 对复杂和孤立的物体进行明确的阶段恢复.
    • 保持了较低的计算复杂性,并显示了动态3D测量的潜力.

    结论:

    • 拟议的物理一致的脱设计克服了端到端回归中的优化冲突.
    • 该方法为单射FPP提供了强大的解决方案,提高了准确性和效率.
    • 对实时动态3D测量应用的验证潜力.