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

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

548
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.
548
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

6.9K
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...
6.9K

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 Analgesic Efficacy and Safety of Intramuscular Hydromorphone Versus Butorphanol for Acute Pain in the Emergency Department: A Randomized Trial.

Pain research & management·2026
Same author

Influence of Southeast China's seawater chemistry on the performance development of cement-sodium silicate grout.

Scientific reports·2026
Same author

Erratum: Wide-field and non-invasive imaging of brain tumours with scattered light techniques: erratum.

Biomedical optics express·2026
Same author

Wide-field and non-invasive imaging of brain tumours with scattered light techniques.

Biomedical optics express·2026
Same author

Intermittent Active Inference.

Entropy (Basel, Switzerland)·2026
Same author

Ultrasound synthetic aperture non-line-of-sight imaging.

Communications physics·2025
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: Jun 3, 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

463

IGAF:增量引导注意力融合用于深度超分辨率.

Athanasios Tragakis1, Chaitanya Kaul2, Kevin J Mitchell1

  • 1School of Physics and Astronomy, University of Glasgow, Glasgow G12 8QQ, UK.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种用于指导深度超分辨率 (GDSR) 的新方法,以使用高分辨率图像来增强低分辨率深度图. 渐进指导注意力融合 (IGAF) 模块在深度地图增强方面取得了最先进的结果.

关键词:
卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.深度超分辨率超级分辨率多模式传感器融合技术

更多相关视频

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
13:01

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

Published on: April 10, 2016

33.9K
Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

297

相关实验视频

Last Updated: Jun 3, 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

463
Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development
13:01

Using Light Sheet Fluorescence Microscopy to Image Zebrafish Eye Development

Published on: April 10, 2016

33.9K
Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

297

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
  • 传感器融合式传感器

背景情况:

  • 准确的深度估计对于机器人,导航和医学成像至关重要.
  • 传统的深度传感器产生低分辨率 (LR) 的深度图,限制了详细的场景感知.
  • 提高LR深度图以高分辨率 (HR) 使用RGB图像等结构化输入是必不可少的.

研究的目的:

  • 为指导深度超分辨率 (GDSR) 提出一种新的传感器融合方法.
  • 开发一个增量引导注意力融合 (IGAF) 模块,以实现有效的特征融合.
  • 创建一个强大的超分辨率模型,从LR输入生成详细的HR深度图.

主要方法:

  • 开发了一种用于导向深度超分辨率 (GDSR) 的新型传感器融合方法.
  • 引入了增量引导注意力融合 (IGAF) 模块,以融合RGB图像和LR深度地图功能.
  • 使用基准数据集IGAF模块构建和评估了一个超级分辨率模型.

主要成果:

  • 与IGAF一起提出的GDSR模型在纽约大学v2数据集上获得了×4,×8和×16上样的最先进的结果.
  • 该模型在Middlebury,Lu和RGB-D-D数据集的零射击设置中超过了所有基线模型.
  • 通过LR深度和HR图像数据的有效融合,证明了准确的HR深度地图生成.

结论:

  • IGAF模块有效地融合了RGB图像和LR深度图的特征,以准确地估计HR深度.
  • 拟议的GDSR方法提供了一个强大的解决方案,以提高深度地图分辨率.
  • 该方法在多个数据集中实现了卓越的性能,突出了其可概括性和有效性.