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Confocal microscopy is an advanced microscopic technique. The prime advantage of the confocal microscope over other microscopy techniques is its ability to block the out-of-focus light from the illuminated samples using pinholes. It is widely used with fluorescence optics to obtain high-resolution, sharp contrast images. Unlike optical microscopes, confocal microscopes use a focused beam of light laser to scan the entire sample surface at different z-planes. These microscopes are, therefore,...
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通过非线性边缘转换进行单拍3D重建:监督和无监督学习方法.

Andrew-Hieu Nguyen1, Zhaoyang Wang2

  • 1Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD 21224, USA.

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本研究介绍了一种使用深度学习和非线性边缘转换的新型单次3D形状重建方法. 使用深度卷积生成对抗网络 (DCGAN) 的无监督学习优于监督方法,可以从单个图像中准确地重建3D对象.

关键词:
深度学习是一种深度学习.边缘投影 边缘投影 边缘投影生成性的对抗性网络.三维成像三维成像技术三维形状测量三维形状测量

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科学领域:

  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 三维重建的3D重建

背景情况:

  • 从二维图像中准确的3D对象表示是计算机视觉的一个关键挑战.
  • 结合结构光和深度学习的进步提供了高质量的3D形状采集.
  • 现有的方法通常需要多个图像或复杂的设置.

研究的目的:

  • 介绍一种新的单射3D形状重建方法.
  • 为非线性边缘转换开发一种深度学习方法.
  • 为了比较监督和无监督的学习这项任务.

主要方法:

  • 一个深度学习网络将灰度边缘输入转换为相位移边缘输出.
  • 结构光边缘投影造型测量用于3D重建.
  • 使用了监督 (UNet) 和无监督 (DCGAN) 学习网络.

主要成果:

  • 与监督的UNet相比,无监督的DCGAN方法显示出优异的图像对图像生成.
  • 拟议的技术可以从单个边缘图像中准确地重建3D形状.
  • 实验验证证证实了该技术的实用性和稳定性.

结论:

  • 开发的单击方法有效地使用非线性边缘转换重建3D形状.
  • 无监督深度学习,特别是DCGAN,对于这种3D重建任务非常有效.
  • 该技术在各种需要单图像3D重建的现实场景中具有广泛的适用性.