Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.6K
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...
7.6K
Confocal Fluorescence Microscopy01:16

Confocal Fluorescence Microscopy

14.3K
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,...
14.3K
Three-Dimensional Microscopy in Microbiology01:28

Three-Dimensional Microscopy in Microbiology

294
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...
294

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Single-shot optically sectioned fluorescence endomicroscopy using unsupervised RCAN-CycleGAN.

Optics express·2026
Same author

Mod-SE(2): a geometric deep learning framework for brain tumor classification and segmentation in MRI images.

Journal of biomedical science·2026
Same author

Metasurface-based Fourier ptychographic microscopy.

Nanophotonics (Berlin, Germany)·2025
Same author

Dynamic underwater gradient-index lens formed by acoustic-vortex-induced cavitation.

Optics letters·2025
Same author

Histogram-based Res-UNet model for optical sectioning HiLo endo-microscopy.

Optics express·2025
Same author

Radial-balanced phase transfer functions for accurate retrieval in quantitative differential phase contrast microscopy.

Optics express·2025
Same journal

Denoising algorithm of Φ-OTDR systems based on adaptive fractional wavelet transform denoising.

Optics express·2026
Same journal

Millisecond photon-to-photon latency and high-speed volumetric projection system for optogenetics.

Optics express·2026
Same journal

Polarization-encoded coaxial structured light for high-precision 3D surface profilometry.

Optics express·2026
Same journal

Discrete freeform optical design based on collaborative optimization of point cloud and local normals.

Optics express·2026
Same journal

Ultrafast ghost imaging with 25 GHz speckle switching and wavelength-division multiplexing.

Optics express·2026
Same journal

Atomic vapor cells fabricated by femtosecond laser welding of standard-optical-quality glass.

Optics express·2026
查看所有相关文章

相关实验视频

Updated: Sep 11, 2025

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
05:22

Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

Published on: June 21, 2024

523

保持空间分辨率的多焦点共焦点光显微镜与深度学习.

Surag Athippillil Suresh, Sunil Vyas, J Andrew Yeh

    Optics express
    |August 13, 2025
    PubMed
    概括
    此摘要是机器生成的。

    深度学习增强了多焦点共焦显微镜,以实现更快,高分辨率的生物成像. 这种方法,使用修改的注意力U-Net,克服了体积成像中的速度分辨率权衡.

    更多相关视频

    Highly Resolved Intravital Striped-illumination Microscopy of Germinal Centers
    10:07

    Highly Resolved Intravital Striped-illumination Microscopy of Germinal Centers

    Published on: April 9, 2014

    10.1K
    Confocal and Super-Resolution Imaging of Polarized Intracellular Trafficking and Secretion of Basement Membrane Proteins During Drosophila Oogenesis
    10:41

    Confocal and Super-Resolution Imaging of Polarized Intracellular Trafficking and Secretion of Basement Membrane Proteins During Drosophila Oogenesis

    Published on: May 19, 2022

    2.3K

    相关实验视频

    Last Updated: Sep 11, 2025

    Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research
    05:22

    Author Spotlight: Enhanced Multiplex Immunofluorescent Microscopy Protocol for Neuroscience Research

    Published on: June 21, 2024

    523
    Highly Resolved Intravital Striped-illumination Microscopy of Germinal Centers
    10:07

    Highly Resolved Intravital Striped-illumination Microscopy of Germinal Centers

    Published on: April 9, 2014

    10.1K
    Confocal and Super-Resolution Imaging of Polarized Intracellular Trafficking and Secretion of Basement Membrane Proteins During Drosophila Oogenesis
    10:41

    Confocal and Super-Resolution Imaging of Polarized Intracellular Trafficking and Secretion of Basement Membrane Proteins During Drosophila Oogenesis

    Published on: May 19, 2022

    2.3K

    科学领域:

    • 生物医学成像技术 生物医学成像技术
    • 计算生物学 计算生物学
    • 显微镜技术 显微镜技术

    背景情况:

    • 混焦显微镜提供高分辨率,但采集速度较慢.
    • 多焦点照明增加了速度,但降低了空间分辨率.
    • 在体积生物样本成像中,成像速度和分辨率之间存在差距.

    研究的目的:

    • 开发用于多焦点共焦点显微镜的深度学习方法.
    • 为了实现更快的图像采集,而不会影响空间分辨率.
    • 解决多焦同焦显微镜中固有的速度分辨率权衡问题.

    主要方法:

    • 使用修改后的U-Net,ResU-Net和Attention U-Net架构实现了一个图像到图像翻译模型.
    • 在生物样本的配对实验数据集上训练和测试模型.
    • 使用常规的共焦图像作为基准真相和多焦图像作为输入.

    主要成果:

    • 修改后的Attention U-Net显著改善了图像质量和结构细节的保留.
    • 注意U-Net实现了更高的信号噪声比率 (32.83dB) 和结构相似度指数 (0.935) 的峰值.
    • 与U-Net相比,空间频率分析证实了低频和高频信息的优越保存.

    结论:

    • 深度学习,特别是注意力U-Net,有效地匹配传统的对焦成像质量,同时增加速度.
    • 开发的方法成功地解决了多焦同焦显微镜中的速度分辨率权衡问题.
    • 这种深度学习的整合显示了对各种非焦点成像应用的巨大潜力.