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
Interference and Superposition of Waves01:07

Interference and Superposition of Waves

4.9K
When two waves of the same nature occur in the same region simultaneously, they result in interference. Interference of waves implies that the net effect of the waves is the sum of the individual waves' effects. However, it does not imply that the individual waves affect the propagation of other waves.
Interference occurs in mechanical waves, such as sound waves, waves on a string, and surface water waves. Mechanical waves correspond to the physical displacement of particles. Hence,...
4.9K
Upsampling01:22

Upsampling

202
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...
202
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

171
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
171
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

550
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
550

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

Grounding surgical action triplets with instrument instance segmentation: a dataset and target-aware fusion approach.

International journal of computer assisted radiology and surgery·2026
Same author

Aligning Few-Step Diffusion Models With Dense Reward Difference Learning.

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

Variational Instance-Adaptive Personalized Stress Recognition Based on Wearable Sensor Signals.

IEEE transactions on bio-medical engineering·2026
Same author

Development and validation of an AI foundation model for endoscopic diagnosis of esophagogastric junction adenocarcinoma: a cohort and deep learning study.

EClinicalMedicine·2025
Same author

An investigation into the causes of race bias in artificial intelligence-based cine cardiac magnetic resonance segmentation.

European heart journal. Digital health·2025
Same author

CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery.

Scientific data·2025
Same journal

Anchor-based disentanglement framework for incremental multi-view clustering.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Complex-valued amplitude-phase interference modeling for adversarially robust classification.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Decentralized ADMM for factorization-based Low-rank matrix estimation.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026
查看所有相关文章

相关实验视频

Updated: Jun 4, 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

8.4K

通过时空特征交互来增强时空视频超分辨率.

Zijie Yue1, Miaojing Shi2

  • 1College of Electronic and Information Engineering, Tongji University, China.

Neural networks : the official journal of the International Neural Network Society
|December 20, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个新的时空网络来增强视频,提高率和图像清晰度. 该方法有效地利用空间和时间的相关性,实现更高的时空视频超分辨率 (STVSR).

关键词:
运动一致性 运动一致性光学流的光学流量空间时间视频超分辨率超高分辨率.空间时间特征的相互作用.

更多相关视频

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

15.6K
Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations
13:13

Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations

Published on: March 19, 2021

2.8K

相关实验视频

Last Updated: Jun 4, 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

8.4K
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

15.6K
Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations
13:13

Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations

Published on: March 19, 2021

2.8K

科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 视频处理 视频处理

背景情况:

  • 时空视频超分辨率 (STVSR) 旨在提高视频的时间和空间分辨率.
  • 当前的深度学习方法通常优先考虑时间相关性而不是空间相关性.
  • 现有的方法可能无法充分利用不同空间分辨率之间的相互作用.

研究的目的:

  • 提出一个新的网络,有效地利用STVSR的空间和时间相关性.
  • 为了增强跨不同和空间分辨率的功能交互.
  • 为了提高时空视频超分辨率的整体性能.

主要方法:

  • 为STVSR开发了一个时空特征交互网络.
  • 引入了一个时空框架插曲模块,用于同时进行低分辨率和高分辨率的特征插曲.
  • 采用时空局部和全球精细化模块来利用特征相关性.
  • 利用一种新的运动一致性损失来确保时间连续性.

主要成果:

  • 拟议的方法显著改进了最先进的STVSR技术.
  • 在标准基准测试 (Vid4,Vimeo-90K,Adobe240) 上的实验结果显示了相当大的性能提升.
  • 该网络有效地利用了空间和时间特征的相关性.

结论:

  • 开发的时空特征交互网络为STVSR提供了显著的进步.
  • 同时利用空间和时间的相关性导致优越的视频增强.
  • 该方法为改善视频率和空间分辨率提供了强大的解决方案.