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

Deconvolution01:20

Deconvolution

146
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
146
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

612
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
612
Reducing Line Loss01:18

Reducing Line Loss

150
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
150

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

Comparative efficacy and toxicity of Axicabtagene Ciloleucel <i>versus</i> Tisagenlecleucel in European patients with large B-cell lymphoma: a systematic review and meta-analysis.

PeerJ·2026
Same author

Adapting KAS-seq for genome-wide transcription profiling in plants.

Trends in plant science·2026
Same author

Psychiatric events induced by roflumilast: a real-world pharmacovigilance study of the FDA Adverse Event Reporting System database.

Frontiers in psychiatry·2026
Same author

A real-world pharmacovigilance study of romidepsin based on FDA adverse event reporting system database.

Frontiers in oncology·2026
Same author

Double-chambered left ventricle in a pediatric patient with tuberous sclerosis complex: A case report.

Pediatric investigation·2026
Same author

Surface Defect-Compensating Thiophene Ligands Reinforce Charge Transport in CsPbI<sub>3</sub> Quantum Dot Solar Cells.

ACS applied materials & interfaces·2026

相关实验视频

Updated: Jun 17, 2025

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

499

盲人深度变化网络 利 利

Zhiyuan Zhang, Haoxuan Li, Chengjie Ke

    IEEE transactions on neural networks and learning systems
    |August 9, 2024
    PubMed
    概括

    这项研究介绍了VBPN,这是一个用于盲板利的深度变异网络. 通过处理未知的图像退化,VBPN提高了多光谱图像的空间分辨率,实现了最先进的结果.

    科学领域:

    • 遥感 遥感 遥感 遥感
    • 计算机视觉 计算机视觉
    • 图像处理 图像处理

    背景情况:

    • 泛化增强了使用泛色图像的多光谱图像分辨率.
    • 现有的深度学习方法与未知或多样化的图像退化作斗争.
    • 现实世界的应用往往涉及到盲目磨场景.

    研究的目的:

    • 开发一种强大的盲点利方法,以解决未知的图像退化问题.
    • 将降解估计和图像融合整合到一个统一的贝叶斯框架中.
    • 提高泛磨技术的概括能力和可解释性.

    主要方法:

    • 提出了一种深度变异网络 (VBPN) 用于盲板研磨.
    • 整合降解估计和图像融合到贝叶斯框架中.
    • 利用神经网络对近似后部分布进行参数化,将降解参数视为隐藏变量.

    主要成果:

    • VBPN有效地估计了多谱和泛色图像的降解参数.
    • 该网络包括降解估计和图像融合子网络,通过变异推理优化结果.
    • 在模拟和现实数据集上实现了最先进的融合性能.

    结论:

    更多相关视频

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.0K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    380

    相关实验视频

    Last Updated: Jun 17, 2025

    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

    499
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.0K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    380
    • 与现有方法相比,VBPN在盲目磨中表现出优异的性能.
    • 该方法将基于模型的解释性与深度学习的灵活性相结合.
    • VBPN为现实世界磨应用提供了更好的概括性和稳定性.