Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

12.2K
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...
12.2K
Upsampling01:22

Upsampling

581
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...
581
Deconvolution01:20

Deconvolution

543
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...
543

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Corrigendum to "Dual-functional hydrogel platform suppresses M1 activation and stabilizes M2 macrophages in intervertebral disc degeneration" [Mater. Today Bio, 38 (2026), 103087].

Materials today. Bio·2026
Same author

Structural and Pharmacological Basis for the State-Dependent Activation of the Autoinhibited <i>P. aeruginosa</i> ClpP2 Protease.

ACS chemical biology·2026
Same author

Dual-functional hydrogel platform suppresses M1 activation and stabilizes M2 macrophages in intervertebral disc degeneration.

Materials today. Bio·2026
Same author

Neuronal Differentiation of GBM-Initiating Cells Combined with Elimination of Undifferentiated Cells Preserves Motor Function.

Cells·2026
Same author

Parthenolide Attenuates Skeletal Muscle Atrophy Through Regulation of Protein Homeostasis and Inhibition of Inflammation.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2026
Same author

Protein compound macrophage migration inhibitory factor for diagnosing postmenopausal osteoporosis.

Frontiers in chemistry·2026

相关实验视频

Updated: Jan 15, 2026

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

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.8K

时空视频超分辨率与神经操作员的神经操作员

Yuantong Zhang, Hanyou Zheng, Daiqin Yang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |October 9, 2025
    PubMed
    概括

    这项研究引入了一种新的基于物理的方法,用于时空视频超分辨率 (STVSR). 它增强了运动估计和对大动作进行补偿,显著提高了视频质量.

    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 信号处理 信号处理

    背景情况:

    • 时空视频超分辨率 (STVSR) 旨在提高空间和时间维度的视频分辨率.
    • 目前的STVSR方法难以对显著物体运动进行准确的运动估计和补偿 (MEMC).
    • 基于物理学的神经网络为模拟复杂的物理过程提供了一个新的范式.

    研究的目的:

    • 开发一种先进的STVSR方法,克服大型运动的MEMC的局限性.
    • 为了利用连续的功能空间和基于物理的原理来改进时空细节的重建.
    • 为增强的视频超分辨率引入一个高效和准确的MEMC机制.

    主要方法:

    • 在STVSR中建模MEMC挑战,作为连续函数空间之间的映射.
    • 将低分辨率的表示转换为高分辨率的表示,具有丰富的时空细节.
    • 设计一个Galerkin类型的注意力功能,以实现高效的框架对齐和时间插值.

    主要成果:

    • 与STVSR.中最先进的技术相比,提出的方法实现了更高的性能.
    • 在固定尺寸和连续的STVSR任务中都表现出有效性.
    • 加勒金类型的注意力机制提供了线性复杂性和全球受感场,用于精确的大型运动估计.

    更多相关视频

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    735
    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
    11:34

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

    Published on: December 3, 2013

    16.0K

    相关实验视频

    Last Updated: Jan 15, 2026

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

    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

    Published on: February 12, 2014

    8.8K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    735
    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
    11:34

    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

    Published on: December 3, 2013

    16.0K

    结论:

    • 这种新的基于物理的方法显著提升了STVSR的能力,特别是在具有大运动的场景中.
    • 开发的Galerkin类型注意力机制为MEMC提供了高效和准确的解决方案.
    • 该方法为STVSR性能设定了一个新的基准,代码公开可用.