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

Color Vision01:24

Color Vision

616
Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
616

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

Leucobacter faecis sp. nov., a Novel Actinomycete Isolated from Bat Feces of Rousettus and Hipposideros spp.

Current microbiology·2026
Same author

Effectiveness of lignans against severe fever with thrombocytopenia syndrome virus infection.

Virologica Sinica·2026
Same author

Association between the Dietary Index for Gut Microbiota and all-cause mortality in coronary heart disease: A retrospective cohort analysis of NHANES (2005-2018).

Medicine·2026
Same author

Emergence of newly reported HIV-1 recombinants CRF120_0107 and CRF149_01B in Guangxi Zhuang Autonomous Region, China: Molecular evidence and public health challenges.

Biosafety and health·2026
Same author

TRA2A negatively regulates HIV-1-induced macrophage pyroptosis by mediating TXNIP expression in an m6A-dependent manner.

Cell death discovery·2026
Same author

Dynamic Biomass-Based Hydrogel with Dual pH/Glucose Responsiveness for Controlled Nitric Oxide Release and Diabetic Wound Healing.

Biomacromolecules·2026

相关实验视频

Updated: Jul 23, 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

581

通过金字塔双重非局部注意力进行无监督的深度标本色化.

Hanzhang Wang, Deming Zhai, Xianming Liu

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |July 13, 2023
    PubMed
    概括

    这项研究介绍了一种新的以实例为基础的色化方法,使用金字塔式的双重非局部注意力网络. 这种方法有效地转移色彩风格和语义信息,实现最先进的摄影现实效果.

    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:

    • 以示例为基础的色化旨在将参考图像的颜色应用于灰度目标图像,保留语义内容和色彩风格.
    • 现有的方法难以有效地利用语义色彩信息并将其集成到目标图像中,通常由于单阶段架构而失去空间细节.

    研究的目的:

    • 开发一种先进的基于实例的色化策略,克服当前方法的局限性.
    • 从参考图像中增强全球色彩风格和语义信息的利用.
    • 改进融合机制,以获得更好的语义一致性和细节保存.

    主要方法:

    • 提出了一个示例性的色化策略,利用金字塔式的双重非局部注意网络来捕捉远程依赖和多尺度的相关性.
    • 实现了对称分支,以便在目标图像和参考图像之间进行特征对齐,加上双向非局部融合策略.
    • 采用无监督学习方法与混合监督 (伪配对和未配对).

    主要成果:

    • 与现有的最先进的技术相比,拟议的方法在照片现实的色彩化方面表现出了卓越的性能.
    • 金字塔双非局部注意网络有效地利用多尺度的相关性和远程依赖性来改善色彩传输.
    • 双向融合策略确保了语义一致性,并提高了彩色图像的视觉质量.

    更多相关视频

    Revealing Neural Circuit Topography in Multi-Color
    09:11

    Revealing Neural Circuit Topography in Multi-Color

    Published on: November 14, 2011

    15.1K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    442

    相关实验视频

    Last Updated: Jul 23, 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

    581
    Revealing Neural Circuit Topography in Multi-Color
    09:11

    Revealing Neural Circuit Topography in Multi-Color

    Published on: November 14, 2011

    15.1K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    442

    结论:

    • 开发的示例色彩化策略在生成视觉上可信和语义上一致的彩色图像方面取得了重大进展.
    • 新的网络架构和无监督学习方法为具有挑战性的色化任务提供了强大的解决方案.
    • 这种方法为基于样本的图像彩色化中的照片现实性结果设定了新的基准.