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

537
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...
537

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

High dietary inflammatory index is associated with an increased risk of overweight and obesity in adults: a meta-analysis of observational studies.

Frontiers in nutrition·2026
Same author

Influencing factors of oral frailty in Chinese maintenance hemodialysis patients: a Bayesian network analysis.

Renal failure·2026
Same author

Development of Predictive Models for NB-UVB Treatment Efficacy and Safety in Psoriasis.

Psoriasis (Auckland, N.Z.)·2026
Same author

Double-stranded RNA and ROS scavenging nanoplatform for modulating skin inflammation.

Nature communications·2026
Same author

Core competencies of clinical nursing educators in the AI era: a qualitative study.

BMC nursing·2026
Same author

Correction: IL-6 is one of the key factors in the formation of gut tissue resident memory T cells from Naïve T cells.

PLoS pathogens·2026

相关实验视频

Updated: Jan 13, 2026

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.7K

哈兹迪夫 (Hazediff):一种无训练的基于扩散的图像脱雾方法,具有像素级特征注入.

Xiaoxia Lin1, Zhengao Li1, Dawei Huang1

  • 1College of Intelligent Equipment, Shandong University of Science and Technology, Taian, China.

PloS one
|October 28, 2025
PubMed
概括

HazeDiff是一种无需培训的图像清除方法,通过消除对数据集的需求,克服了当前方法的局限性. 这种基于扩散模型的技术提高了图像质量和概括性,以实现更清晰的视觉任务.

更多相关视频

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope
14:09

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

Published on: April 7, 2014

16.1K
Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

2.3K

相关实验视频

Last Updated: Jan 13, 2026

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.7K
Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope
14:09

Quantitative Optical Microscopy: Measurement of Cellular Biophysical Features with a Standard Optical Microscope

Published on: April 7, 2014

16.1K
Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
07:15

Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

Published on: July 11, 2025

2.3K

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:

  • 经常出现的雾现象会降低图像质量,妨碍中高层次的视觉任务.
  • 当前的图像处理方法在数据采集和概括方面面临着挑战.

研究的目的:

  • 提出HazeDiff,一种基于扩散模型的无培训的图像脱雾方法.
  • 克服现有的数据依赖的除尘技术的局限性.

主要方法:

  • 哈兹迪夫使用了扩散模型,消除了对配对训练数据的需求.
  • 像素级特征注入 (PFI) 将参考图像特征集成到扩散过程中.
  • 结构保留模型 (SRM) 增强特征并保持结构完整性.

主要成果:

  • 在真实世界和合成数据集上,HazeDiff超越了最先进的方法.
  • 在没有参考 (NIQE) 和完全参考 (PSNR) 指标上获得更高的分数.
  • 在恢复高质量的图像方面表现出卓越的概括能力和实用性.

结论:

  • 哈泽迪夫提供了一个可靠和有效的解决方案,用于图像 dehazing.
  • 无培训方法降低了计算成本,并提高了模型稳定性.
  • 恢复后的图像呈现出自然的视觉特征和清晰的结构内容.