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

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

6.8K
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...
6.8K

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

Characterization and preliminary heterosis evaluation of novel wheat genetically divergent populations.

Frontiers in plant science·2026
Same author

Nitrogen-Mediated Orbital-Compatible π-Extension: Balancing Excited-State Components and Suppressing Vibrational Broadening Toward Redshifted Narrowband MR-TADF Emitters.

Angewandte Chemie (International ed. in English)·2026
Same author

Research progress on the biological activity and applications of gamma-aminobutyric acid.

Food research international (Ottawa, Ont.)·2026
Same author

SFRP2 drives aerobic glycolysis and tumor progression in ovarian cancer by transcriptional upregulation of PTK2B.

Journal of translational medicine·2026
Same author

The universal stress protein SlUSP1 modulates Botrytis cinerea resistance via ATP-dependent chitinase activation in tomato.

Plant science : an international journal of experimental plant biology·2026
Same author

A β-1,3-glucanase antagonizes a phase-separating WRKY repressor to confer saline-alkaline tolerance in rice.

The New phytologist·2026

相关实验视频

Updated: May 21, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.4K

基于[公式:参见文本]N的远程传感多图像超分辨率的研究.

Wenxin Liu1, Shengbing Che2, Wanqin Wang1

  • 1College of Computer Science and Mathematics, Central South University of Forestry & Technology, Changsha, 410004, Hunan, China.

Scientific reports
|March 20, 2025
PubMed
概括

这项研究引入了一种新的深度学习模型,用于增强远程传感图像分辨率. 拟议的网络有效地融合了空间和时间特征,大大提高了图像质量和细节,以便更好地分析.

关键词:
[公式:参见文本]N个网络模型ABFE的空间特征提取在CRFM的时间空间特征融合中.多图像超分辨率的超级分辨率.遥感图像的远程传感图像

更多相关视频

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
08:41

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

11.5K
Super-resolution Imaging of the Bacterial Division Machinery
08:47

Super-resolution Imaging of the Bacterial Division Machinery

Published on: January 21, 2013

11.8K

相关实验视频

Last Updated: May 21, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.4K
Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
08:41

Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution

Published on: August 16, 2012

11.5K
Super-resolution Imaging of the Bacterial Division Machinery
08:47

Super-resolution Imaging of the Bacterial Division Machinery

Published on: January 21, 2013

11.8K

科学领域:

  • 地球观测 地球观测
  • 计算机视觉 计算机视觉
  • 地理空间分析的研究.

背景情况:

  • 高分辨率 (HR) 遥感图像至关重要,但由于传感器的局限性和成本,其获取具有挑战性.
  • 现有的超分辨率方法往往难以有效地整合空间和时间信息.

研究的目的:

  • 开发一个先进的深度学习模型,用于多图像遥感超分辨率.
  • 提高时空特征的提取和融合,以改善图像重建.

主要方法:

  • 提出了端到端的多图像遥感超分辨率与增强的时空特征交互融合网络 (N).
  • 使用基于注意的特征编码器 (ABFE) 与道注意区块 (CAB) 进行空间特征提取.
  • 实施了剩余时间注意区块 (RTAB) 和ConvGRU-RTAB融合模块 (CRFM) 用于时间特征建模和融合.

主要成果:

  • 在PROBA-V数据集上,达到49.69dB (NIR) 和51.57dB (RED) 的峰值信号噪声比 (cPSNR) 中取得了显著的改进.
  • 与TR-MISR和MAST等最先进的方法相比,在重建图像中显示出更高的视觉质量.

结论:

  • [公式:见文本]N模型有效地解决了HR遥感图像采集的局限性.
  • 拟议的方法为高质量的超分辨率重建提供了强大的解决方案,提高了遥感数据的实用性.