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

Deconvolution01:20

Deconvolution

484
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...
484
Blind Procedures02:07

Blind Procedures

12.7K
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...
12.7K
Visual Agnosia01:12

Visual Agnosia

750
Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
750

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

Enhancing Underwater Light Field Images via Global Geometry-Aware Diffusion Process.

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

QMSANet: A quaternion multi-scale attention network for robust color image denoising.

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

AI-Powered Monitoring of the Acute: Chronic Workload Ratio: Interpretable Injury Risk Prediction in Soccer Players.

Sports health·2026
Same author

The evolution of high-order genome architecture revealed from 1,000 species.

Cell·2026
Same author

SpatialCOC: an integrative framework for spatial continuous mapping and cross-omics correction in spatial multi-omics data.

Nature communications·2026
Same author

Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers.

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

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

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

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

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

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

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

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

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

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

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

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
查看所有相关文章

相关实验视频

Updated: Dec 21, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

539

深度变化网络 走向盲目的图像恢复

Zongsheng Yue, Hongwei Yong, Qian Zhao

    IEEE transactions on pattern analysis and machine intelligence
    |February 13, 2024
    PubMed
    概括
    此摘要是机器生成的。

    这项研究介绍了一种新的盲人图像恢复方法,该方法结合了基于模型和深度学习的方法. 这项新技术使用贝叶斯生成模型和变异推理来实现优越的图像无色化和超分辨率.

    更多相关视频

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.8K
    Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline
    09:27

    Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

    Published on: January 30, 2019

    7.1K

    相关实验视频

    Last Updated: Dec 21, 2025

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    539
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.8K
    Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline
    09:27

    Measuring the Shape and Size of Activated Sludge Particles Immobilized in Agar with an Open Source Software Pipeline

    Published on: January 30, 2019

    7.1K

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习

    背景情况:

    • 盲人图像恢复 (IR) 是一个具有挑战性的计算机视觉问题.
    • 现有的方法包括基于经典模型和深度学习 (DL) 的方法,每个方法都有局限性.

    研究的目的:

    • 提出一种新的盲视图像恢复方法,整合基于模型和DL技术的优势.
    • 开发一个统一的框架,用于联合降解估计和图像恢复.

    主要方法:

    • 为盲 IR 构建了一个一般的贝叶斯生成模型,明确定义了降解过程.
    • 雇佣了一个像素智能的非i.i.d. 用于灵活的图像噪声建模的高斯分布.
    • 设计了一个带有深度神经网络的变异推理算法,用于后置分布参数化.

    主要成果:

    • 与最先进的方法相比,拟议的方法在图像消除和超分辨率方面取得了卓越的性能.
    • 统一的框架有效地整合了退化估计和图像恢复.
    • 灵活的噪声建模处理复杂的图像退化类型.

    结论:

    • 新的贝叶斯生成模型与变异推理提供了一种有效的方法来盲目恢复图像.
    • 该方法在关键的IR任务中显示出与现有技术相比的显著改进.
    • 这一综合框架推进了计算机视觉领域的图像质量提升.