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

197
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...
197
Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

6.7K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
6.7K
Downsampling01:20

Downsampling

192
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
192
Masking and Demasking Agents01:19

Masking and Demasking Agents

2.5K
EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on...
2.5K

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

Multi-Wavelength Biometric Acquisition System Utilizing Finger Vasculature NIR Imaging.

Sensors (Basel, Switzerland)·2023
Same author

SUTO-Solar Through-Turbulence Open Image Dataset.

Sensors (Basel, Switzerland)·2022
Same author

A Contemporary Design Process for Single-Phase Voltage Source Inverter Control Systems.

Sensors (Basel, Switzerland)·2022
Same author

Modified Distance Transformation for ImageEnhancement in NIR Imaging of Finger Vein System.

Sensors (Basel, Switzerland)·2020

相关实验视频

Updated: Jul 24, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K

基于自动编码器的单色图像去除训练策略的比较.

Piotr Jóźwik-Wabik1, Krzysztof Bernacki1, Adam Popowicz1

  • 1Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
概括

机器学习有效地消除单色图像,即使没有清晰的数据示例. 这种方法超越了当前的方法,为改善科学成像中的图像质量提供了强大的解决方案.

科学领域:

  • 图像处理 图像处理
  • 机器学习是机器学习.
  • 科学成像科学成像

背景情况:

  • 单色图像对于信号强度分析至关重要.
  • 图像噪声显著降低了对象识别和强度估计.
  • 像非本地平均值和块匹配3D这样的确定性识别算法是最先进的.

研究的目的:

  • 探索机器学习用于单色图像无色化.
  • 在各种数据可用性场景中评估ML性能,包括有限或没有无噪声数据.
  • 评估培训策略和数据集特征对否定有效性的影响.

主要方法:

  • 使用简单的自动编码器架构进行图像无声化.
  • 在MNIST和CIFAR-10数据集上训练并测试了自动编码器.
  • 对自动编码器模型研究了不同的培训方法.

主要成果:

  • 机器学习的无效性能受到训练方法,自动编码器架构和数据集图像相似性的影响.
  • 拟议的ML方法实现的性能超过了当前最先进的确定性算法.
  • 即使训练数据缺乏无噪声示例,也可以有效地消除噪声.

结论:

关键词:
高斯式噪声 (Gaussian noise) 是一种高斯式噪声.自动编码器自动编码器图像去色化 图像去色化

更多相关视频

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

444

相关实验视频

Last Updated: Jul 24, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.2K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

444
  • 机器学习为单色图像无色化提供了一个有希望和有效的方法.
  • 基于ML的脱应该被认为是传统方法的可行替代方案.
  • 对ML培训策略的进一步研究可以为特定应用优化denoising.