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相关概念视频

IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Infrared (IR) Spectroscopy: Overview01:09

Infrared (IR) Spectroscopy: Overview

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When electromagnetic radiation passes through a material, atoms or molecules transition from a lower to a higher energy state by absorbing radiation corresponding to the energy difference between the two states. The absorption of infrared (IR) radiation causes transitions between vibrational energy levels in a molecule. Therefore, IR spectroscopy is a useful analytical tool for determining the molecular structure of molecules.
Different compounds display unique properties due to their...
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IR Frequency Region: X–H Stretching01:24

IR Frequency Region: X–H Stretching

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In IR spectroscopy, signals produced by the X−H bonds (such as C−H, O−H, or N−H) can be observed in the frequency range of  2700–4000 cm–1. The C−H stretching vibration forms sharp bands in the region 2850–3000 cm–1. The presence of the O−H stretching vibration leads to the forming of an absorption band in the frequency range 3650–3200 cm−1. At the same time, N−H stretching can be confirmed by absorption bands in...
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相关实验视频

Updated: May 21, 2025

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

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双编码器 基于网络的窄带未冷却的红外成像 剥离网络

Minghe Wang1, Pan Yuan1, Su Qiu1

  • 1MOE Key Laboratory of Optoelectronic Imaging Technology and System, Beijing Institute of Technology, Beijing 100081, China.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的双编码器网络,以消除危险气体泄漏检测中的红外图像. 该方法有效地消除混合噪声,显著提高工业安全应用的图像质量.

关键词:
采用双编码器的UNet网络.红外焦平面探测器 红外焦平面探测器热成像消除噪音的方法没有冷却的红外线.

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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相关实验视频

Last Updated: May 21, 2025

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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科学领域:

  • 红外成像技术 红外成像技术
  • 信号处理 信号处理
  • 人工智能的人工智能

背景情况:

  • 未冷却的红外成像对于检测危险的气体泄漏至关重要.
  • 这些系统中的窄带波器捕获低事件能量,导致混合噪声 (固定的模式和高斯式).
  • 现有的消噪方法对这种复合噪声无效,阻碍了气体羽毛的识别.

研究的目的:

  • 开发用于窄带未冷却红外图像的先进无网络.
  • 为了有效地应对红外气体泄漏检测中混合噪声的挑战.
  • 为了提高信号与噪声的比率,以便更清楚地识别气体雾.

主要方法:

  • 设计了一个UNet结构的双编码器网络.
  • 两个编码器,基本尺度剩余注意力 (BSRA) 和扩大尺度剩余注意力 (ESRA),被开发用于捕获不同尺度的噪音.
  • 来自两个尺度的噪声特征被融合并由解码器处理用于图像重建.

主要成果:

  • 拟议的网络有效地消除了窄带未冷却的红外图像中的复合噪声.
  • 客观和主观评估证实了与现有方法相比的优异性能.
  • 实现了信号与噪声比的显著提升.

结论:

  • 开发的双编码器网络提供了一个强大的解决方案,用于消除受混合噪声影响的红外图像.
  • 这项技术在改善危险气体泄漏检测,遥感和医学成像方面具有重大潜力.
  • 该方法增强了未冷却红外成像系统在关键工业和科学领域的实际应用.