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相关概念视频

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

7.0K
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...
7.0K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

94
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
94
Upsampling01:22

Upsampling

242
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
242
Deconvolution01:20

Deconvolution

168
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...
168
Downsampling01:20

Downsampling

167
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...
167

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相关实验视频

Updated: Jul 13, 2025

Lensless Fluorescent Microscopy on a Chip
11:23

Lensless Fluorescent Microscopy on a Chip

Published on: August 17, 2011

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对于复杂域像素超分辨率的深度非局部低级规范化.

Hanwen Xu, Daoyu Li, Xuyang Chang

    Optics letters
    |October 13, 2023
    PubMed
    概括

    这项研究引入了一种新的复杂域非局部低级网络 (CNLNet),用于相位成像中的像素超分辨率 (PSR). CNLNet显著提高了重建质量和对噪声的稳定性,优于现有的方法.

    科学领域:

    • 光学和光子学 在光学和光子学.
    • 图像处理 图像处理
    • 计算成像技术的成像

    背景情况:

    • 像素超分辨率 (PSR) 通过克服采样限制来增强相位成像.
    • 现有的PSR算法由于相位检索和超分辨率的非凸度而与噪声灵敏度作斗争.
    • 非本地低级 (NLR) 正规化为PSR提供了更高的准确性和稳定性.

    研究的目的:

    • 开发一种更强大,更准确的像素超分辨率 (PSR) 技术,用于相位成像.
    • 引入一种新的规范化方法,灵感来自NLR先验,以提高重建质量.
    • 通过在深度特征领域运行来解决传统NLR的局限性.

    主要方法:

    • 实施了一个插入和运行框架,其中包含PSR的非本地低级 (NLR) 规范化.
    • 开发了复杂域非本地低级网络 (CNLNet) 的规范化.
    • 在深度特征域中通过CNLNet.net进行非本地相似性匹配和低级近似.

    主要成果:

    • 与传统的NLR相比,基于CNLNet的重建显示出更高的性能.
    • 与传统的NLR方法相比,实现了平均1.4dB的PSNR改进.
    • 在各种具有挑战性的场景中超越现有的PSR算法.

    更多相关视频

    Super-resolution Imaging of Neuronal Dense-core Vesicles
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    相关实验视频

    Last Updated: Jul 13, 2025

    Lensless Fluorescent Microscopy on a Chip
    11:23

    Lensless Fluorescent Microscopy on a Chip

    Published on: August 17, 2011

    17.7K
    Super-resolution Imaging of Neuronal Dense-core Vesicles
    09:30

    Super-resolution Imaging of Neuronal Dense-core Vesicles

    Published on: July 2, 2014

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    Ground State Depletion Super-resolution Imaging in Mammalian Cells
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    Ground State Depletion Super-resolution Imaging in Mammalian Cells

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    结论:

    • 拟议的CNLNet规范化为相位成像中的精确和强大的像素超分辨率提供了最先进的解决方案.
    • 在深度特征域中运行比空间域方法提供了显著的优势.
    • 在相位成像重建中,CNLNet代表了克服噪声限制的重大进步.