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

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

90
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
90

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

The association of inflammatory markers with cerclage failure in twin pregnancies.

BMC pregnancy and childbirth·2026
Same author

Integrated metagenomic and metabolomic profiling of spontaneous preterm birth in Chinese women.

Frontiers in microbiology·2026
Same author

Identification of a Novel MTM1 Mutation Associated with X-Linked Myotubular Myopathy: Clinical and Molecular Insights for Prenatal Diagnosis.

International journal of women's health·2026
Same author

Mood symptoms, insomnia during pregnancy, and adverse neonatal outcomes.

Acta psychologica·2026
Same author

Two-stitch versus one-stitch cervical cerclage in women with high risk for preterm birth: a stratified exploratory randomized controlled trial in China.

BMC pregnancy and childbirth·2026
Same author

Physical examination-indicated repeat cerclage in singleton pregnancies: a retrospective cohort study.

BMC pregnancy and childbirth·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
查看所有相关文章

相关实验视频

Updated: Jul 23, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

442

基于生成对抗网络的预处理框架的水下图像增强方法.

Xiao Jiang1, Haibin Yu1,2, Yaxin Zhang1

  • 1College of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
概括
此摘要是机器生成的。

这项研究介绍了ECO-GAN,这是机器人有效的水下图像增强方法. 它有效地解决了色彩扭曲,低对比度和运动模糊,改善了水下摄影.

关键词:
卷积神经网络 (CNN) 是一种神经网络.跨阶段的核聚变.功能提取 特性提取生成性的对抗性网络 (GANs)水下图像增强水下图像增强

更多相关视频

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

633
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

581

相关实验视频

Last Updated: Jul 23, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

442
Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
09:19

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Published on: April 18, 2025

633
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

581

科学领域:

  • 计算机视觉 计算机视觉
  • 机器人技术 机器人技术 机器人技术
  • 图像处理 图像处理

背景情况:

  • 水下机器人摄影会受到色彩扭曲,低对比度和运动模糊的影响.
  • 现有的方法可能无法同时全面解决多个水下图像退化问题.

研究的目的:

  • 为机器人摄影开发一种高效的水下图像增强方法.
  • 为了应对包括色彩扭曲,低对比度和运动模糊在内的挑战.

主要方法:

  • 提出ECO-GAN,一个基于生成对抗网络的预处理框架.
  • 利用卷积神经网络准运动模糊,低亮度和颜色偏差.
  • 采用编码器-解码器架构与跨阶段融合模块,以优化性能.

主要成果:

  • ECO-GAN有效地同时进行无噪声,消除模糊和消除色彩偏差.
  • 与现有方法相比,实现了优越的水下图像增强.
  • 证明了扩展到多个水下图像增强功能的灵活性.

结论:

  • ECO-GAN提供了一种高效和有效的解决方案,用于增强水下机器人图像.
  • 该方法可以在不需要先前的物理知识的情况下实现盲人图像增强.
  • 对于推进自主水下勘探和数据收集,ECO-GAN显示出有前途的前景.