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

14.6K
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...
14.6K
The Uncertainty Principle04:08

The Uncertainty Principle

31.9K
Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
31.9K
Blind Procedures02:07

Blind Procedures

13.5K
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
13.5K
Uncertainty in Measurement: Reading Instruments02:46

Uncertainty in Measurement: Reading Instruments

52.7K
Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
52.7K
Blinding01:11

Blinding

4.0K
Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.
4.0K
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

414
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
414

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

CuDi: Curve Distillation for Efficient and Controllable Exposure Adjustment.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

3D-UIR: 3D Gaussian for Underwater 3D Scene Reconstruction via Physics-Based Appearance-Medium Decoupling.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Bio-heterojunction-engineered recombinant collagen hydrogel orchestrates multimodal sterilization and immunomodulation for MRSA-infected wound healing.

Bioactive materials·2026
Same author

Novel deep learning-based prediction of HER2 expression in breast cancer using multimodal MRI, nomogram, and decision curve analysis.

Frontiers in oncology·2025
Same author

Assessment of Habitat Suitability for Amphioxus in the Changli Marine Reserve and Adjacent Coastal Waters, Hebei Province.

Animals : an open access journal from MDPI·2025
Same journal

Style-Aware Contrastive Test-Time Adaptation: A Dual-Cache Model for Robust Vision-Language Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Semantic Frame Interpolation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Physics-Guided Cross-Modal Decoupling with Test-Time Adaptation for Hyperspectral Image Restoration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
查看所有相关文章

相关实验视频

Updated: Feb 5, 2026

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

Super-resolution Imaging of Neuronal Dense-core Vesicles

Published on: July 2, 2014

10.1K

结合不确定性指导和Top-k代码库匹配,用于现实世界盲图像超分辨率的超分辨率.

Weilei Wen, Tianyi Zhang, Qianqian Zhao

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |February 3, 2026
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种用于真实图像超分辨率 (SR) 的新框架,该框架可以提高纹理细节和特征匹配精度. 不确定性引导和Top-k代码库匹配SR (UGTSR) 方法提高了重建图像的真实性.

    更多相关视频

    Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging
    10:01

    Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging

    Published on: September 8, 2017

    8.2K
    Super-resolution Imaging of the Bacterial Division Machinery
    08:47

    Super-resolution Imaging of the Bacterial Division Machinery

    Published on: January 21, 2013

    12.2K

    相关实验视频

    Last Updated: Feb 5, 2026

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

    Super-resolution Imaging of Neuronal Dense-core Vesicles

    Published on: July 2, 2014

    10.1K
    Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging
    10:01

    Demonstration of a Hyperlens-integrated Microscope and Super-resolution Imaging

    Published on: September 8, 2017

    8.2K
    Super-resolution Imaging of the Bacterial Division Machinery
    08:47

    Super-resolution Imaging of the Bacterial Division Machinery

    Published on: January 21, 2013

    12.2K

    科学领域:

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

    背景情况:

    • 基于代码书的超分辨率 (SR) 方法显示出对现实世界应用的希望.
    • 现有的技术在准确的特征匹配和纹理重建方面扎.

    研究的目的:

    • 提出一个新的不确定性导向和Top-k代码库匹配SR (UGTSR) 框架.
    • 为了解决SR中特征匹配精度和纹理细节重建的局限性.

    主要方法:

    • 整合了一个不确定性学习机制,以专注于纹理丰富的地区.
    • 利用Top-k特征匹配策略,通过融合候选特征来提高准确性.
    • 使用了Align-Attention模块来改善低分辨率 (LR) 和高分辨率 (HR) 功能之间的信息对齐.

    主要成果:

    • 在纹理现实主义方面表现出显著的改进.
    • 与现有的SR方法相比,实现了增强的重建保真性.
    • 拟议的UGTSR框架的性能超过了当前最先进的技术.

    结论:

    • 该UGTSR框架有效地解决了基于代码书的SR的关键挑战.
    • 该方法导致更现实的和准确的图像重建,特别是在纹理细节.
    • 未来的工作可以建立在不确定性引导和Top-k匹配策略的基础上,以提高SR性能.