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Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Updated: Jun 24, 2026

Measuring Spatially- and Directionally-varying Light Scattering from Biological Material
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物理驱动的计算多光谱成像用于准确的颜色测量.

Haoyu Yi1, Mingwei Zhou2, Hao Xie1

  • 1College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
概括

这项研究引入了一个深度学习框架,用于从RGB图像中准确测量牙颜色,克服照明挑战. 该方法精确预测光谱反射率,改善视觉应用中的颜色保真度.

关键词:
颜色测量 颜色测量 测量深度学习是一种深度学习.超光谱成像技术的使用.物理信息化的网络网络.这是光谱反射率.

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科学领域:

  • 计算机视觉 计算机视觉
  • 材料科学 材料科学 材料科学
  • 生物医学成像技术 生物医学成像技术

背景情况:

  • 精确的颜色测量对于基于视觉的系统至关重要,但由于可变的照明,复杂性和主观性而受到阻碍.
  • 由于严格的感知和光谱保真性要求,牙颜色测量存在独特的挑战.

研究的目的:

  • 开发一个深度学习框架,用于从RGB图像中准确的快照光谱反射率预测.
  • 在复杂的照明场景中解决现有色彩测量技术的局限性,使用牙科应用作为验证案例.

主要方法:

  • 一个深度学习的,端到端的光谱反射率预测框架,利用物理可解释的特征融合网络.
  • 开发一个双重注意的模块化信息融合神经网络,直接从RGB图像中恢复光谱反射.
  • 使用定制光学系统在复杂照明下创建一个数据集,包含4000个RGB-超光谱图像对.

主要成果:

  • 拟议的框架在预测牙光谱反射率方面取得了很高的准确性,平均平方误差 (MSE) 为0.0024,结构相似度指数 (SSIM) 为0.8724.
  • 在自然牙和陶材料的多种场景中表现出有效的性能.
  • 在复杂的照明条件下,成功地从RGB图像中恢复了光谱反射.

结论:

  • 深度学习框架为准确的颜色测量提供了强大的解决方案,克服了由metamerism引起的颜色不匹配.
  • 这一进步使得增强的光学属性表征,3D表面重建和计算机辅助的修复设计成为可能.
  • 物理可解释的网络设计有助于物理知情的特征融合,以改善光谱预测.