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

6.9K
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...
6.9K

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

VariantMedium: sensitive and generalizable somatic point mutation calling with 3D DenseNets trained and evaluated on experimental data.

Genome medicine·2026
Same author

Explainable artificial intelligence in prostate cancer treatment recommendation: A decision support system for oncological expert panels.

European journal of cancer (Oxford, England : 1990)·2026
Same author

FloraSyntropy-net: scalable deep learning with novel FloraSyntropy archive for large-scale plant disease diagnosis.

Plant methods·2026
Same author

Development and validation of an automated algorithm for palatal rugae matching in forensic identification.

Journal of dentistry·2026
Same author

Comic: explainable drug repurposing via contrastive masking for interpretable connections.

BMC bioinformatics·2026
Same author

Multi-omics driven computational framework for cancer molecular subtype classification.

Scientific reports·2025

相关实验视频

Updated: Jun 9, 2025

Ground State Depletion Super-resolution Imaging in Mammalian Cells
07:55

Ground State Depletion Super-resolution Imaging in Mammalian Cells

Published on: November 5, 2017

7.2K

扩散模型,图像超分辨率和一切:一项调查

Brian B Moser, Arundhati S Shanbhag, Federico Raue

    IEEE transactions on neural networks and learning systems
    |October 29, 2024
    PubMed
    概括
    此摘要是机器生成的。

    扩散模型 (DMs) 提升了图像超分辨率 (SR),获得了高质量的结果. 本综述统一了SR的DM基础,解决了挑战并探索了未来的研究方向.

    更多相关视频

    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

    8.4K
    Super-resolution Imaging of the Bacterial Division Machinery
    08:47

    Super-resolution Imaging of the Bacterial Division Machinery

    Published on: January 21, 2013

    11.8K

    相关实验视频

    Last Updated: Jun 9, 2025

    Ground State Depletion Super-resolution Imaging in Mammalian Cells
    07:55

    Ground State Depletion Super-resolution Imaging in Mammalian Cells

    Published on: November 5, 2017

    7.2K
    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

    8.4K
    Super-resolution Imaging of the Bacterial Division Machinery
    08:47

    Super-resolution Imaging of the Bacterial Division Machinery

    Published on: January 21, 2013

    11.8K

    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 图像处理 图像处理

    背景情况:

    • 扩散模型 (DMs) 显著提高了图像超分辨率 (SR),在现实主义和感知质量方面超过了以前的生成方法.
    • 尽管取得了成功,但SR中的DM面临着诸多挑战,包括高计算成本,可比性问题,缺乏可解释性和颜色变化.

    研究的目的:

    • 为图像超分辨率应用的扩散模型的理论基础提供统一的概述.
    • 分析SR领域的DM的独特特征和方法,与更广泛的审查区分开来.

    主要方法:

    • 图像超分辨率中的扩散模型的综合文献综述和理论分析.
    • 探索当前的研究趋势,包括替代输入领域,条件化技术,指导机制,腐败空间和零射击学习.

    主要成果:

    • 扩散模型为图像超分辨率提供了强大的框架,实现了最先进的感知质量.
    • 该审查巩固了对DM原则及其对SR的具体应用的理解.

    结论:

    • 这项工作旨在消除图像超分辨率的扩散模型领域的神秘性,使其更容易获得.
    • 通过强调挑战和未来的研究途径,本文旨在利用扩散模型促进SR领域的创新.