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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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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
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适应式编码相罩设计和高质量的图像重建,用于无干扰的编码光圈对应全息.

Rui Xiong, Xiangchao Zhang, Xinyang Ma

    Optics express
    |April 4, 2024
    PubMed
    概括

    一种新的深度学习方法通过同时减少系统噪声和背景组件来增强编码的光圈关联全息. 这提高了信号与噪声的比率,使3D对象的成像更清晰.

    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
    • 机器学习应用 机器学习应用

    背景情况:

    • 编码的光圈关联全息提供非扫描,不连贯的3D成像.
    • 挑战包括由相罩编码的系统噪声和重建的背景文物.

    研究的目的:

    • 开发一种基于深度学习的方法,用于在编码的光圈关联全息中同时减轻系统噪声和背景组件.
    • 为了提高重建的3D图像的信号噪声比 (SNR).

    主要方法:

    • 两个子网络的深度学习方法:一个编码的相罩设计子网络和一个图像重建子网络.
    • 设计子网络使用对象频率分布生成自适应编码相罩.
    • 重建子网络映射对象的全息图自相对应,结合物理知识来抑制背景.

    主要成果:

    • 拟议的方法有效地抑制了系统噪声和背景组件.
    • 重建图像的信号噪声比 (SNR) 显著改善.
    • 深度学习方法提高了全息重建过程的适应性和解释性.

    结论:

    • 这种基于深度学习的方法成功地解决了编码光圈关联全息的关键挑战.

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  • 这种技术为噪音和背景降低提供了强大的解决方案,从而实现更高质量的3D成像.
  • 这项研究强调了人工智能在推进全息成像技术方面的潜力.