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

7.6K
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...
7.6K

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

Clinical characteristics and outcomes of advanced EGFR-mutated NSCLC treated with 45 or 30 mg starting doses of dacomitinib: a retrospective multicenter analysis.

Therapeutic advances in medical oncology·2026
Same author

Coronavirus M Protein Hijacks Toll-Interacting Protein (TOLLIP) to Suppress NF-κB Signaling and Promote Immune Evasion.

MedComm·2026
Same author

SARS-CoV-2 Nsp1 suppresses the canonical NF-κB pathway by promoting ubiquitin-dependent degradation of TAK1 kinase.

PLoS pathogens·2026
Same author

Accurate profiling of single-cell alternative transcript start sites by correcting RNA degradation.

Nature communications·2026
Same author

Copper supports regulatory T cell energetic state to sustain peripheral immune tolerance.

Science immunology·2026
Same author

Comprehensive immune profiling reveals IFN-γ signaling in T cells mediates parasite phagocytosis in a rodent malaria model.

mBio·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: Sep 11, 2025

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

8.5K

逐步结构保护和细节精细化用于远程传感单图像超分辨率的远程传感.

Wei-Yen Hsu, Shih-Hao Huang, Jing-Wen Lin

    IEEE transactions on neural networks and learning systems
    |August 18, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新的深度学习模型,用于远程传感图像超分辨率,可保存结构和细节. 渐进式结构保护和细节精细化超分辨率 (PSPDR-SR) 模型通过提高结构完整性和细节来提高图像质量.

    更多相关视频

    Test Samples for Optimizing STORM Super-Resolution Microscopy
    16:52

    Test Samples for Optimizing STORM Super-Resolution Microscopy

    Published on: September 6, 2013

    31.2K
    Super-Resolution Live Cell Imaging of Subcellular Structures
    06:50

    Super-Resolution Live Cell Imaging of Subcellular Structures

    Published on: January 13, 2021

    4.9K

    相关实验视频

    Last Updated: Sep 11, 2025

    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

    8.5K
    Test Samples for Optimizing STORM Super-Resolution Microscopy
    16:52

    Test Samples for Optimizing STORM Super-Resolution Microscopy

    Published on: September 6, 2013

    31.2K
    Super-Resolution Live Cell Imaging of Subcellular Structures
    06:50

    Super-Resolution Live Cell Imaging of Subcellular Structures

    Published on: January 13, 2021

    4.9K

    科学领域:

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

    背景情况:

    • 传统的深度学习模型用于远程传感图像超分辨率 (RSISR) 通常在上采样时丢失信息,限制图像质量.
    • 遥感图像的复杂性挑战了结构完整性和精细纹理的保护.
    • 变压器模型改进了全球特征捕获,但可能是多余的,错过了局部细节.

    研究的目的:

    • 为增强的RSISR提出一种新的渐进结构保护和细节精细化超分辨率 (PSPDR-SR) 模型.
    • 为了提高结构完整性和精细细节的保存在超高分辨率的遥感图像.
    • 解决现有模型在处理复杂的遥感图像特征方面的局限性.

    主要方法:

    • 该PSPDR-SR模型使用两个子网络:结构意识超分辨率 (SaSR) 和细节恢复和精细化 (DR&R).
    • 粗至细动态信息传输 (C2FDIT) 和细至粗动态信息传输 (F2CDIT) 模块利用多层和多尺度的功能.
    • 动态信息传输模块 (DITM) 集成变压器和卷积长短期内存 (ConvLSTM) 进行双向特征传输.

    主要成果:

    • 在基准数据集上,PSPDR-SR模型表现出高于最先进的方法的性能.
    • 定量和定性评估显示,在结构保存和细节增强方面有显著的改进.
    • 像SSIM,MS-SSIM,LPIPS,DISTS,SCC和SAM这样的关键指标证实了该模型的有效性.

    结论:

    • 拟议的PSPDR-SR模型通过保护结构和精细细节,有效地提高了遥感图像的超分辨率.
    • 动态信息传输模块促进了全面的功能融合,并减轻了冗余性.
    • PSPDR-SR为高保真遥感图像重建提供了一个有前途的解决方案.