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相关概念视频

Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

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Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
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Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

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Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
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Three-Dimensional Microscopy in Microbiology01:28

Three-Dimensional Microscopy in Microbiology

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Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...
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Total Internal Reflection Fluorescence Microscopy01:05

Total Internal Reflection Fluorescence Microscopy

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Total internal reflection fluorescence microscopy or TIRF is an advanced microscopic technique used to visualize fluorophores in samples close to a solid surface with a higher refractive index, such as a glass coverslip. TIRF only allows fluorophores in proximity to the solid surface to be excited. When light from a medium with a lower refractive index (such as air) hits the glass coverslip at a critical angle, the light undergoes total internal reflection stead of passing through the glass.
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Two-Dimensional Microscopy in Microbiology01:29

Two-Dimensional Microscopy in Microbiology

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Two-dimensional (2D) microscopy encompasses a range of optical techniques that capture images within a single focal plane, offering detailed representations of microscopic structures. These techniques are essential in biological and medical research, enabling the visualization of cellular and subcellular structures with different levels of contrast and specificity.There are several major types of 2D microscopy, each with strengths and applications.Bright-Field MicroscopyBright-field microscopy...
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Protein Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
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From Fast Fluorescence Imaging to Molecular Diffusion Law on Live Cell Membranes in a Commercial Microscope
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深度学习增强光显微镜通过共聚焦物理成像模型.

Baoyuan Zhang, Xuefeng Sun, Jialuo Mai

    Optics express
    |June 29, 2023
    PubMed
    概括

    这项研究引入了一种新的深度学习方法,用于对焦显微镜图像重建. 通过模拟图像退化,它提高了分辨率和保真度,超过了传统的解卷方法.

    科学领域:

    • 显微镜和成像科学 显微镜和成像科学
    • 计算生物学 计算生物学
    • 光学物理学的光学物理学

    背景情况:

    • 同焦显微镜对于生物学和工业中的高分辨率成像至关重要.
    • 深度学习有助于微图重建,但经常忽略成像物理,导致别名问题.
    • 现有的方法在多尺度图像对对齐和概括方面扎.

    研究的目的:

    • 开发用于对焦显微镜图像重建的深度学习方法,解决别名并提高保真度.
    • 将基于物理原理的图像降解模型集成到深度学习中,以提高性能.
    • 在网络培训中消除对精确图像对齐的需求.

    主要方法:

    • 使用理查德斯-沃尔夫矢量衍射积分和对焦成像理论开发了一种图像降解模型.
    • 通过使用物理模型降低高分辨率对应的低分辨率训练图像.
    • 结合了残余神经网络与轻量级特征注意力模块和降解模型.

    主要成果:

    • 在对焦图像重建中实现了高保真性和概括性.
    • 性能优于非负最小平方和理查德森-卢西解卷算法.
    • 达到了0.82以上的结构相似性指数,并提高了0.6dB以上的峰值信号噪声比.

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  • 在不同的深度学习网络中证明了广泛的适用性.
  • 结论:

    • 拟议的物理信息深度学习模型显著提高了对焦显微镜图像重建质量.
    • 这种方法减轻了别名化问题,并与传统方法相比改善了概括性.
    • 该方法在需要高准确度显微镜成像的各种应用中显示出前景.