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

7.0K
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...
7.0K

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

Development of a 3D nnU-Net-based cell tracking platform for quantifying myocardial deformation in zebrafish.

iScience·2026
Same author

Dietary Intake of Processed Meats with Fermented Foods: Effects on Carcinoembryonic Antigen, Hematological Parameters, and Gut Microbiota of Adult and Elderly Mouse Models.

Food science of animal resources·2026
Same author

NPAS3-regulated astrocyte mitochondrial bioenergetics is required for cognition.

Science advances·2026
Same author

Six-month follow-up after Effects of Resistance Training on Osteosarcopenia in postmenopausal Korean women (ERTO-K study): a randomized controlled trial.

Gerontology·2026
Same author

Market Status of Meat Analogs and Their Impact on Livestock Industries.

Food science of animal resources·2026
Same author

The Color-Developing Methods for Cultivated Meat and Meat Analogues: A Mini-Review.

Food science of animal resources·2026

相关实验视频

Updated: Jun 29, 2025

Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display
09:04

Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display

Published on: January 14, 2020

9.7K

HoloSR:基于深度学习的超级分辨率,用于实时高分辨率的计算机生成全息图.

Siwoo Lee, Seung-Woo Nam, Juhyun Lee

    Optics express
    |April 4, 2024
    PubMed
    概括
    此摘要是机器生成的。

    HoloSR通过使用深度学习从低分辨率的RGBD图像生成高分辨率全息图来增强3D成像. 这种新的方法可以实现实时,现实的3D图像制作,无需插值.

    更多相关视频

    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

    12.3K
    Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging
    10:01

    Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging

    Published on: September 8, 2017

    7.7K

    相关实验视频

    Last Updated: Jun 29, 2025

    Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display
    09:04

    Recording Ultra-Realistic Full-Color Analog Holograms for Use in a Moving Hologram Display

    Published on: January 14, 2020

    9.7K
    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

    12.3K
    Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging
    10:01

    Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging

    Published on: September 8, 2017

    7.7K

    科学领域:

    • 计算机视觉 计算机视觉
    • 全息影像的使用方法.
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 从低分辨率数据生成高分辨率的计算机生成全息图 (CGH) 是一个挑战.
    • 实时3D图像重建需要高效的超分辨率技术.

    研究的目的:

    • 介绍HoloSR,一种基于深度学习的超级分辨率方法,用于CGH.
    • 为了从低分辨率的RGBD图像中直接生成高分辨率的CGH.

    主要方法:

    • HoloSR集成了一个增强的深度超分辨率网络,具有调整尺寸和卷积层.
    • 该方法直接产生高分辨率的CGH,无需插值.
    • 使用定量指标 (SSIM,PSNR) 评估了高达×4的升级规模.

    主要成果:

    • HoloSR成功地实现了CGH生成的超分辨率.
    • 从低分辨率的RGBD输入中有效生成高分辨率全息图.
    • 通过模拟和实验结果验证.

    结论:

    • 在CGH中,HoloSR为超分辨率提供了一种新且有效的方法.
    • 该方法促进实时生成现实的3D图像.
    • 适用于监督和无监督学习场景.