Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Super-resolution Fluorescence Microscopy

Super-resolution Fluorescence Microscopy

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...

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

Qualitative analysis of edible oil mixture for omega-3 content using terahertz time-domain spectroscopy.

NPJ science of food·2025

Same author

Enhancing cognitive and physical performance in older adults through wearable sensor-based interactive cognitive-motor training: a randomized clinical trial.

Scientific reports·2025

Same author

THz-Wave Absorption Properties of Organic-Inorganic Hybrid Perovskite Materials: A New Candidate for THz Sensors.

Small science·2025

Same author

Brain alterations and neurologic disorder progression induced by lymphatic dysfunction in the head and neck region.

Acta neuropathologica communications·2025

Same author

Terahertz biomedical imaging using a terahertz emitter based on InAs nanowires.

Nanoscale·2025

Same author

Deep learning-driven macroscopic AI segmentation model for brain tumor detection via digital pathology: Foundations for terahertz imaging-based AI diagnostics.

Heliyon·2024

Search research articles

相关实验视频

Updated: Jul 3, 2025

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

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

Published on: November 30, 2022

J-Net:改进了U-Net,用于特拉赫兹图像超分辨率.

Woon-Ha Yeo^1,2, Seung-Hwan Jung^1,2, Seung Jae Oh³

¹Department of Artificial Intelligence Convergence, Sahmyook University, 815 Hwarang-ro, Nowon-gu, Seoul 01795, Republic of Korea.

Sensors (Basel, Switzerland)

|February 10, 2024

概括

研究人员开发了一个新的深度学习网络J-Net,以提高太赫兹 (THz) 图像分辨率. 这种方法可显著提高各种应用的图像质量,优于现有的超高分辨率技术.

关键词:

卷积神经网络 (CNN) 是一种神经网络.深度学习是一种深度学习.图像超分辨率的超级分辨率太赫兹图像的图像

更多相关视频

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

相关实验视频

Last Updated: Jul 3, 2025

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

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

Published on: November 30, 2022

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

科学领域:

电磁波应用电磁波应用
图像处理和计算机视觉

背景情况:

太赫兹 (THz) 波 (0.110 THz) 用于安全,生物医学和材料检查.
由于波长长,THz图像的低分辨率限制了它们的实际应用.
提高THz图像分辨率是一个关键的研究挑战.

研究的目的:

介绍J-Net,一种用于增强太赫兹图像超分辨率的新型网络架构.
为了有效地提取低分辨率的特征,并将它们映射到高分辨率图像.

主要方法:

提出了J-Net,这是一个增强的U-Net架构,具有简单的基线块.
在DIV2K+Flickr2K数据集上训练了网络.
通过峰值信号噪声比率 (PSNR) 和视觉检查来评估性能.

主要成果:

J-Net 实现了 32.52 dB 的 PSNR,超过其他方法超过 1 dB.
与现有技术相比,在真实世界THz图像上表现出卓越的性能.
在超分辨率THz图像中显示显著的视觉改进.

结论:

简网有效地解决了低分辨率THz成像的挑战.
拟议的架构为THz图像超分辨率提供了卓越的定量和质量结果.
J-Net代表了THz成像技术的重大进步.