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

Delayed Bile Duct Injury Preceded by Sepsis After Biliary Cooling-Assisted Ablation of Hilar Hepatocellular Carcinoma.

ACG case reports journal·2026
Same author

Central fractalkine-CX3CR1 signaling mediates systemic LPS-induced inhibition of LH pulses in female rats.

Endocrinology·2026
Same author

Age-Group Differences in Ball Velocity and Spin Rate Among Youth Baseball Players: A Cross-Sectional Study Using a Portable Tracking Device.

International journal of sports physiology and performance·2026
Same author

Contrast-Enhanced Ultrasound and Somatostatin Receptor Scintigraphy Unveil an Occult Neuroendocrine Tumor With Carcinoid Syndrome and Presumed Small Intestinal Origin - A Case Report.

Gastro hep advances·2026
Same author

Gas-forming Pyogenic Liver Abscess Mimicking Gastric Perforation in a Patient With Diabetic Ketoacidosis and Subsequent Rupture: A Case Report.

Gastro hep advances·2026
Same author

Living sensor display implanted on skin for long-term biomarker monitoring.

Nature communications·2026

相关实验视频

Updated: Jun 11, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K

通过深度学习辅助的代算法进行图形相检索.

Koki Yamada1, Natsuki Akaishi1, Kohei Yatabe1

  • 1Department of Electrical Engineering and Computer Science Tokyo University of Agriculture and Technology 2-24-16 Naka-cho, Koganei Tokyo Japan.

Journal of applied crystallography
|October 10, 2024
PubMed
概括
此摘要是机器生成的。

一种新的混合阶段检索方法将深度神经网络 (DNN) 与用于ptychography的代算法相结合. 这种方法提高了图像质量和稳定性,即使数据有限,光线低.

关键词:
深度神经网络是一个神经网络.基于公式的监督学习.硬X射线图形图像学 硬X射线图形学阶段检索检索阶段检索

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
Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States
06:25

Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States

Published on: January 19, 2024

929

相关实验视频

Last Updated: Jun 11, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.7K
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
Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States
06:25

Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States

Published on: January 19, 2024

929

科学领域:

  • 计算机成像成像技术
  • 在X射线显微镜中使用X射线显微镜.
  • 阶段检索算法 阶段检索算法

背景情况:

  • 图形摄影是一种强大的计算成像技术,对于显微镜分析至关重要.
  • 阶段检索算法对于图像摄影的成像质量至关重要.
  • 基于深度神经网络 (DNN) 的方法提供了改进的阶段检索,但在实验变化和数据收集方面存在局限性.

研究的目的:

  • 开发一个强大的图形相检索算法,克服DNN的局限性.
  • 为了提高成像质量,并减少图形学中的计算需求.
  • 提高基于DNN的方法对不同实验条件的适应性.

主要方法:

  • 一种混合方法,将基于模型的代算法 (例如,ePIE) 与基于DNN的Denoiser结合起来.
  • 使用合成数据的公式驱动监督方法训练DNN denoiser,避免需要测量样本图像.
  • 通过硬X射线图形学和现实世界数据集的模拟来评估方法.

主要成果:

  • 与传统的EPIE和rPIE相比,提出的混合方法可以重建具有更高空间分辨率的图像.
  • 以一半的代次数实现可比或优越的图像质量.
  • 证明了对超参数的稳定性和低照明强度数据的有效性.
  • 从具有较低重叠比率的数据集中成功重建图像.

结论:

  • 混合DNN-代算法在图形相检索方面取得了重大进展.
  • 这种方法提高了稳定性,减少了数据需求,并提高了成像性能.
  • 它为各种图形图形应用提供了更具适应性和效率的解决方案,包括那些具有具有挑战性的实验条件的应用.