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相关概念视频

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

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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...
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相关实验视频

Updated: Jul 15, 2025

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
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Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

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深度学习辅助的同时目标传感和超高分辨率成像

Jin Zhao1, Huangzhao Zhang2, Ming-Zhe Chong1

  • 1State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics, Peking University, Beijing 100871, China.

ACS applied materials & interfaces
|September 27, 2023
PubMed
概括
此摘要是机器生成的。

这项研究介绍了用于超表面系统的多功能深度学习网络. 该网络准确地感知目标属性,并产生超高分辨率图像,推进电磁传感和成像.

关键词:
深度学习是一种深度学习.反向的重建重建.metasurfaces 是一个地表.感应感应感应 感应感应超高分辨率成像成像技术

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Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
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Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy
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Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 9, 2013

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相关实验视频

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

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

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Lensfree On-chip Tomographic Microscopy Employing Multi-angle Illumination and Pixel Super-resolution
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Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy
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Simultaneous Multicolor Imaging of Biological Structures with Fluorescence Photoactivation Localization Microscopy

Published on: December 9, 2013

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科学领域:

  • 电磁学 电磁学 电磁学 电磁学
  • 地表表面技术的技术.
  • 深度学习应用程序

背景情况:

  • 超表面允许对电磁波进行操纵,用于传感和成像.
  • 超表面集成增加了检索目标信息的复杂性.
  • 现有的深度学习方法通常集中在单个功能上,限制了多功能性.

研究的目的:

  • 开发一个多功能深度学习网络,用于Metasurface-target交互系统.
  • 为了证明网络在感知目标属性和超高分辨率成像方面的能力.
  • 探索深度学习,用于反向重建和前向电磁预测.

主要方法:

  • 一项初步实验验证了在超表面场景中的噪声耐受性.
  • 捕获的电场分布由多功能深度学习网络处理.
  • 为前进的电磁预测开发了一个单独的网络.

主要成果:

  • 网络准确地感知了目标量和相对允许度.
  • 目标的精确超高分辨率图像被生成.
  • 提出的深度学习方法在反向重建和前预测方面表现出有效性.

结论:

  • 多功能深度学习网络为地表系统中目标信息恢复提供了一种多功能方法.
  • 这种方法加速了目标传感和超级成像方面的进步.
  • 深度学习对各种电磁应用具有显著的前景,包括反向和前向问题.