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相关概念视频

Deconvolution01:20

Deconvolution

186
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...
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Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)01:15

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

380
Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...
380
¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

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

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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...
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Potential Due to a Polarized Object01:29

Potential Due to a Polarized Object

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A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...
434
Curvilinear Motion: Polar Coordinates01:27

Curvilinear Motion: Polar Coordinates

388
In polar coordinates, the motion of a particle follows a curvilinear path. The radial coordinate symbolized as 'r,' extends outward from a fixed origin to the particle, while the angular coordinate, 'θ,' measured in radians, represents the counterclockwise angle between a fixed reference line and the radial line connecting the origin to the particle.
The particle's location is described using a unit vector along the radial direction. Deriving the particle's position...
388
Downsampling01:20

Downsampling

182
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...
182

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Pol2Pol:自主监督的极度测量图像去噪声.

Hedong Liu, Xiaobo Li, Zhenzhou Cheng

    Optics letters
    |September 14, 2023
    PubMed
    概括

    这项研究介绍了极化对极化 (Pol2Pol),一种自主监督的方法,用于仅使用一个噪音图像来否定极度图像. Pol2Pol实现了与监督方法可比的性能,而不需要清洁的图像对进行训练.

    科学领域:

    • 计算机视觉 计算机视觉
    • 图像处理 图像处理
    • 光学是什么?光学是什么?光学是什么?

    背景情况:

    • 极度成像捕获丰富的信息,但容易受到噪音的影响.
    • 传统的无色化方法通常需要配对杂和干净的图像.
    • 自主监督学习为图像剥夺提供了一个有希望的替代方案.

    研究的目的:

    • 开发一种自主监督的方法,用于极度度图像消噪.
    • 为了实现有效的无噪声,只使用单个,噪声极度测量图像.
    • 创建一个可扩展和兼容的Denoising框架.

    主要方法:

    • 提出了一种新的极化发生器,可以从单个噪音图像中创建合成训练对.
    • 为了数据合成,利用了固有的极化关系.
    • 适应现有的监督拒绝网络用于自我监督的Pol2Pol框架.

    主要成果:

    • Pol2Pol有效地通过单个噪音输入来消除极度图像.
    • 该方法的性能优于现有的自我监督的消噪技术.
    • 实现了与传统的监督退税方法相提并论的性能.

    结论:

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    • 自主监督的极度测量图像消噪是可行的和有效的.
    • 波尔2波尔方法提供了一种灵活而强大的图像消光方法.
    • 这种技术减少了对极度对称成像中广泛配对训练数据的需求.