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使用超快速卷积光学神经网络进行无记忆散射成像.

Yuchao Zhang1, Qiming Zhang1, Haoyi Yu1

  • 1Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200093, China.

Science advances
|June 14, 2024
PubMed
概括

研究人员通过开发一种新的光学神经网络 (ONN) 通过散射介质实现了无记忆的图像重建. 这一突破使得在具有挑战性的环境中能够实现先进的成像,而无需依赖光学记忆效应.

科学领域:

  • 光学和光子学 在光学和光子学.
  • 机器学习 机器学习
  • 图像重建 图像的重建

背景情况:

  • 光学记忆效应对于像生物组织这样的复杂散射介质中的成像至关重要.
  • 缺乏这种效果的媒体中的图像重建一直是一个重大挑战.
  • 现有的方法在不依赖光学记忆效应的情况下,与强散射作斗争.

研究的目的:

  • 通过光学记忆效应不存在的散射层来演示图像重建.
  • 开发一种新的光学神经网络 (ONN),用于无内存的图像重建.
  • 为了克服当前成像技术在高度散射环境中的局限性.

主要方法:

  • 开发一个多阶段卷积光学神经网络 (ONN).
  • 多个平行内核的集成,以光速运行.
  • 训练基于富里埃光学的ONN,使用强的散射过程直接提取特征.

主要成果:

  • 通过散射层实现了无内存的图像重建.
  • 视野扩大了多达271的因素.
  • 证明了用于超快,多任务图像重建的动态重新配置性.

结论:

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  • 建立了一个超快速和节能的光学机器学习平台.
  • 在没有光学记忆效应的散射介质中启用了图像重建.
  • 为先进的光学图形处理和机器学习应用铺平了道路.