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相关概念视频

Deconvolution01:20

Deconvolution

198
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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Computed Tomography01:10

Computed Tomography

4.6K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

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Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
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Compact Lens-less Digital Holographic Microscope for MEMS Inspection and Characterization
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在全息断层扫描中使用深度学习进行相位解封.

Michał Gontarz, Vibekananda Dutta, Małgorzata Kujawińska

    Optics express
    |June 29, 2023
    PubMed
    概括

    这项研究引入了一个深度学习管道,用于全息断层扫描阶段解封,显著提高噪音,不规则图像的准确性. 新的U-Net架构与注意门和剩余块提供了一个强大的,自动化解决方案,用于阶段数据处理.

    科学领域:

    • 光学和光子学 在光学和光子学.
    • 图像处理 图像处理
    • 人工智能的人工智能

    背景情况:

    • 全息断层扫描 (HT) 产生了对于3D重建至关重要的相位图像.
    • 阶段解封是必不可少的,但由于噪音和HT数据中的不规则,这是一项挑战.
    • 传统的阶段解封方法往往是缓慢的,不可靠的,缺乏自动化.

    研究的目的:

    • 开发一种强大,自动化,高效的全息断层扫描相解封方法.
    • 解决传统相解封算法在处理杂和复杂的实验数据方面的局限性.

    主要方法:

    • 基于U-Net架构的两步卷积神经网络 (CNN) 管道被提出.
    • 管道包括一个排污的阶段,其后是阶段解封的阶段.
    • 注意门 (AG) 和剩余块 (RB) 已集成到U-Net中,以提高解封性能.

    主要成果:

    • 拟议的深度学习管道成功地解开了HT的高度不规则和杂的相位图像.
    • 通过AG和RB增强的U-Net架构在阶段解封中表现出更好的性能.
    • 这是第一个完全在现实HT实验图像上训练的深度学习解决方案.

    结论:

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    • 开发的深度学习管道在全息断层扫描阶段处理方面取得了重大进展.
    • 该方法为分相解封提供了耐噪,可靠和潜在的可自动化解决方案.
    • 这些发现为使用全息断层扫描技术进行更准确,更高效的3D重建铺平了道路.