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超表面授权的快照超光谱成像与凸/深 (CODE) 小数据学习理论

Chia-Hsiang Lin1,2, Shih-Hsiu Huang3, Ting-Hsuan Lin4

  • 1Department of Electrical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan. chiahsiang.steven.lin@gmail.com.

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概括

这项研究介绍了一种使用元光学和深度学习的紧型高光谱成像仪. 这种新的系统高效地产生了详细的超谱数据与最低的培训,使先进的材料识别.

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科学领域:

  • 光学和光子学 在光学和光子学.
  • 材料科学 是一种材料科学.
  • 计算机视觉 计算机视觉

背景情况:

  • 超光谱成像对于材料识别至关重要,但受到大型传统系统的限制.
  • 现有的超表面解决方案减少了体积,但面临着制造复杂性和巨大的足迹.
  • 对于更广泛的应用,需要紧而高效的超光谱成像系统.

研究的目的:

  • 为了开发一个紧的快照超光谱成像仪.
  • 将元光学与小数据深度学习理论相结合.
  • 以最少的培训来证明高保真度的超频谱数据生成.

主要方法:

  • 开发了一种使用单个多波长超表面芯片 (500-650 nm) 的快照超光谱成像仪.
  • 将元光学与小数据凸/深 (CODE) 深度学习理论结合起来.
  • 使用4带多谱数据集和只有18个训练数据点来训练系统.

主要成果:

  • 与传统系统相比,实现了显著减少的设备面积.
  • 有效地生成了一个高保真度的18带超频谱数据立方体.
  • 证明了CODE深度学习在有限数据的超光谱重建中的有效性.

结论:

  • 多共振元面和小数据学习理论的整合使得紧的超频谱成像仪成为可能.
  • 这种方法克服了以前的超表面设计的局限性.
  • 开发的系统有望用于科学和工业中低调的先进仪器.