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

Three-Dimensional Microscopy in Microbiology01:28

Three-Dimensional Microscopy in Microbiology

68
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...
68
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.4K
Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
2.4K
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.1K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.1K

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

Enhancing Infrared Optical Flow Network Computation through RGB-IR Cross-Modal Image Generation.

Sensors (Basel, Switzerland)·2024
Same author

Spatial and Channel Aggregation Network for Lightweight Image Super-Resolution.

Sensors (Basel, Switzerland)·2023
Same author

An Automatic Calibration Method for the Field of View Aberration in a Risley-Prism-Based Image Sensor.

Sensors (Basel, Switzerland)·2023
Same author

Comparison of High-Speed Polarization Imaging Methods for Biological Tissues.

Sensors (Basel, Switzerland)·2022
Same author

Two-year-supervised resistance training prevented diabetes incidence in people with prediabetes: A randomised control trial.

Diabetes/metabolism research and reviews·2019
Same author

Preliminary evaluation of a predictive controller for a rotary blood pump based on pulmonary oxygen gas exchange.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine·2019

相关实验视频

Updated: Jul 24, 2025

Author Spotlight: Non-Invasive Imaging of Complex Bio-Structures Using Polarization-Sensitive Two-Photon Microscopy
05:54

Author Spotlight: Non-Invasive Imaging of Complex Bio-Structures Using Polarization-Sensitive Two-Photon Microscopy

Published on: September 8, 2023

1.2K

通过极化成像和深度学习进行三维形状重建.

Xianyu Wu1, Penghao Li1, Xin Zhang1

  • 1School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习方法用于3D成像,该方法可以改善表面正常估计和纹理细节恢复. 先进的极化成像技术在被动照明下提高了3D重建的准确性.

关键词:
深度学习是一种深度学习.两极化成像成像技术由极化形成的形状.表面正常估计表面正常估计.

更多相关视频

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.1K
Determining 3D Flow Fields via Multi-camera Light Field Imaging
14:25

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

16.7K

相关实验视频

Last Updated: Jul 24, 2025

Author Spotlight: Non-Invasive Imaging of Complex Bio-Structures Using Polarization-Sensitive Two-Photon Microscopy
05:54

Author Spotlight: Non-Invasive Imaging of Complex Bio-Structures Using Polarization-Sensitive Two-Photon Microscopy

Published on: September 8, 2023

1.2K
Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.1K
Determining 3D Flow Fields via Multi-camera Light Field Imaging
14:25

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

16.7K

科学领域:

  • 计算机视觉 计算机视觉
  • 光学成像技术的成像
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 深度学习的3D成像估计了被动照明下的表面正常.
  • 由于信息丢失,现有的方法在纹理细节和正常准确性方面扎.

研究的目的:

  • 为了增强纹理细节的恢复和3D成像中表面正常估计的准确性.
  • 为了减轻在对象重建期间在精细纹理区域的信息丢失.

主要方法:

  • 使用基于斯托克斯向量的参数和分离的反射元件来优化极化输入.
  • 开发网络以提取全面的极化特征并减少背景噪声.

主要成果:

  • 拟议的方法显著提高了表面正常估计的准确性.
  • 与UNet相比,平均角度误差被证明减少了19%.
  • 实现了62%的计算时间缩短和11%的模型尺寸缩小.

结论:

  • 这种新的方法通过增强极化特征提取来提供更准确的3D重建.
  • 优化极化表示导致在表面正常估计和纹理恢复方面具有卓越的性能.