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相关概念视频

Downsampling01:20

Downsampling

154
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
154
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.0K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.0K
¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

1.1K
The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
1.1K
Deconvolution01:20

Deconvolution

159
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...
159

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相关实验视频

Updated: Jun 29, 2025

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

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频率补偿扩散模型用于现实场景脱雾.

Jing Wang1, Songtao Wu1, Zhiqiang Yuan2

  • 1Sony Research and Development Center Beijing Lab, Chao-Yang District, Beijing, 100027, China.

Neural networks : the official journal of the International Neural Network Society
|April 5, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种用于图像脱雾的新型扩散模型,增强了对现实世界雾的概括性. 它包含一个频率补偿块和HazeAug管道,显著提高了对具有挑战性的数据集的性能.

关键词:
数据合成数据的合成.消毒 消毒 消毒 消毒扩散模型的扩散模型.频率补偿的使用费用.

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Systems Analysis of the Neuroinflammatory and Hemodynamic Response to Traumatic Brain Injury

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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科学领域:

  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 图像处理 图像处理

背景情况:

  • 由于分布转移,图像处理的深度学习模型在真实世界的数据上降解.
  • 标准扩散模型在学习细节重建至关重要的高频组件方面面临挑战.

研究的目的:

  • 开发一个强大的图像除雾框架,并改进了对现实世界的一般化.
  • 为了解决扩散模型中的光谱偏差问题,以更好地恢复图像细节.

主要方法:

  • 一个新的频率补偿区块 (FCB) 被设计为强调中高频率.
  • 引入了一个数据增强管道HazeAug,以增加雾的多样性和程度.
  • 为盲目排气建立了一个条件扩散模型框架.

主要成果:

  • 与FCB集成的扩散模型在感知和扭曲指标上显示出显著的改进.
  • 哈兹奥格管道增强了除模型的一般化能力.
  • 拟议的模型在具有挑战性的现实数据集 (如Dense-Haze和Nh-Haze) 上实现了超过1dB的PSNR改进.

结论:

  • 拟议的条件扩散模型与FCB和HazeAug提供了卓越的性能,用于现实世界的图像dehazing.
  • 该框架表现出强大的概括能力,性能优于最近的方法.
  • 这项工作为图像 dehazing 中的分布转移问题提供了有效的解决方案.