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

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

Photonic decision making using optical frequency difference detection in mutually-coupled semiconductor lasers.

Optics express·2026
Same author

Compressive multi-beam scanning transmission electron microscopy.

Ultramicroscopy·2026
Same author

Compressive spectral video by dynamic spatial-spectral-temporal windowed codification.

Optics express·2026
Same author

Demonstration of <b>3</b>.5 × 10<sup>-13</sup> laser frequency stability at 1000 s using an iodine-filled hollow-core fiber photonic microcell.

Optics express·2026
Same author

Compressive event camera.

Optics express·2025
Same author

Boosting microparticle tracking with neuromorphic cameras by optical modulation.

Scientific reports·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: May 14, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.2K

通过基于深度学习的动态散射介质进行快照视频.

Felipe Guzmán, Esteban Vera, Ryoichi Horisaki

    Optics express
    |April 12, 2025
    PubMed
    概括
    此摘要是机器生成的。

    我们开发了一个深度学习模型,通过散射介质从一个动态对象的图像中重建多个. 这种先进的方法以高精度实现了8倍的压缩,提高了成像能力.

    更多相关视频

    Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
    09:04

    Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

    Published on: February 23, 2018

    9.4K
    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
    11:34

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

    Published on: December 3, 2013

    15.6K

    相关实验视频

    Last Updated: May 14, 2025

    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
    10:16

    Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

    Published on: February 8, 2014

    12.2K
    Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture
    09:04

    Lens-free Video Microscopy for the Dynamic and Quantitative Analysis of Adherent Cell Culture

    Published on: February 23, 2018

    9.4K
    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
    11:34

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

    Published on: December 3, 2013

    15.6K

    科学领域:

    • 光学和光子学 在光学和光子学.
    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 通过散射介质进行动态对象成像是具有挑战性的,原因是光的扭曲.
    • 传统方法在高压缩比和复杂的散射环境下扎.
    • 压缩传感具有潜力,但需要复杂的重建算法.

    研究的目的:

    • 开发一个端到端的深度学习模型,从单个快照中重建多个.
    • 为了使动态物体在未知,时间变化的散射条件下实现高速成像.
    • 为了实现显著的数据压缩 (高达8X),同时保持高图像保真度.

    主要方法:

    • 集成一个编码的孔径压缩时间成像系统.
    • 开发一种基于变压器的专用卷积神经网络 (CNN),用于解复和重建.
    • 使用双输入CNN架构,结合斑点模式及其自相关性.

    主要成果:

    • 从单个快照中成功重建了多达八个.
    • 在模拟和实验中证明了高达8X的压缩比.
    • 实现了高的重建质量和准确性,经过废除研究验证.
    • 与单个输入模型相比,双输入CNN显著提高了重建准确性.

    结论:

    • 提出的深度学习模型有效地从压缩的单一快照数据中重建动态场景.
    • 编码光圈成像和先进的CNN集成为通过散射介质进行高速成像提供了强大的解决方案.
    • 双输入CNN方法对于在复杂的散射环境中最大限度地提高重建精度至关重要.