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相关概念视频

Deconvolution01:20

Deconvolution

188
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...
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Visual Agnosia01:12

Visual Agnosia

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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...
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Perceptual Constancy01:12

Perceptual Constancy

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Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
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Masking and Demasking Agents01:19

Masking and Demasking Agents

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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...
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Prosopagnosia01:24

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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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相关实验视频

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Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
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视觉质量驱动的无人监督的图像去.

Aiping Yang1, Yumeng Liu2, Jinbin Wang2

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin, China; Shanghai Artificial Intelligence Laboratory, China.

Neural networks : the official journal of the International Neural Network Society
|August 20, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的轻量级无人监督的除雾网络,可以从图像中去除雾,而不需要配对清洁图像. 该方法使用交互式融合模块和代优化,实现与监督方法相似的结果.

关键词:
图像消毒 图像消毒交互式核聚变技术 交互式核聚变技术代增强是一种代增强.没有监督的学习学习.视觉质量驱动的视觉质量驱动

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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:

  • 现有的基于学习的脱雾方法通常需要大量的模糊和清洁图像的配对数据集,这些图像很难获得.
  • 合成数据的训练可以导致一个领域的转移,当应用到现实世界的模糊图像.
  • 需要有效的无监督的除烟技术,不依赖于配对数据.

研究的目的:

  • 提出一种新的,轻量级的,无人监督的除烟网络,能够直接从模糊的输入中预测清晰的图像.
  • 解决数据采集和领域转移现有方法的局限性.
  • 开发一种强大的除尘解决方案,在没有参考数据的真实世界图像上表现良好.

主要方法:

  • 拟议的网络包括一个交互式融合模块 (IFM) 结合多层次的功能和一个代优化模块 (IOM) 改进 dehazed 结果.
  • 四个非参考,视觉质量驱动的损失功能 (黑暗通道损失,对比损失,和损失,边缘度损失) 旨在促进无监督的训练.
  • 该方法直接运行在模糊图像上,消除了对应清洁图像的需求.

主要成果:

  • 在合成和现实世界数据集上进行了广泛的实验.
  • 与最先进的无监督除烟技术相比,提出的无监督方法表现良好.
  • 该方法的性能与一些监督的除烟方法相提并论,如峰值信号与噪声比率 (PSNR),结构相似度指数 (SSIM) 和通用图像质量指数 (UQI) 等指标所示.

结论:

  • 开发的轻量级无监督除雾网络有效地去除雾,而不需要配对的训练数据.
  • 这种新的方法,利用IFM,IOM和质量驱动损失函数,克服了合成数据训练和域移动问题的局限性.
  • 该方法显示了对现实世界应用的巨大潜力,这些应用需要高效准确的图像处理.