Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media
Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media
复制标题
DOI：
10.1364/optica.5.001181
复制
发表时间：
2018-10-20
期刊：
影响因子：
10.4
通讯作者：
Tian, Lei
中科院分区：
文献类型：
--
作者：
Li, Yunzhe;Xue, Yujia;Tian, Lei
通过散射成像是一个重要而具有挑战性的问题。利用确定性的输入输出“传输矩阵”的固定介质已经取得了巨大的进展。然而，这种“一对一”映射对散斑去相关非常敏感-对散射介质的小扰动导致模型误差和成像性能的严重退化。我们的目标是开发一个新的框架，是高度可扩展的介质扰动和测量要求。为此，我们提出了一种统计“一对多”深度学习（DL）技术，该技术封装了广泛的统计变化，使模型能够适应斑点去相关。具体来说，我们开发了一个卷积神经网络（CNN），它能够学习在一组具有相同宏观参数的漫射器上捕获的散斑强度图案中包含的统计信息。然后，据我们所知，我们第一次证明，经过训练的CNN能够通过一组完全不同的同类扩散器进行泛化和高质量的对象预测。我们的工作铺平了道路，通过散射介质成像的高度可扩展的DL方法。(C)根据OSA开放获取出版协议的条款，2018年美国光学学会
Imaging through scattering is an important yet challenging problem. Tremendous progress has been made by exploiting the deterministic input-output "transmission matrix" for a fixed medium. However, this "one-to-one" mapping is highly susceptible to speckle decorrelations - small perturbations to the scattering medium lead to model errors and severe degradation of the imaging performance. Our goal here is to develop a new framework that is highly scalable to both medium perturbations and measurement requirement. To do so, we propose a statistical "one-to-all" deep learning (DL) technique that encapsulates a wide range of statistical variations for the model to be resilient to speckle decorrelations. Specifically, we develop a convolutional neural network (CNN) that is able to learn the statistical information contained in the speckle intensity patterns captured on a set of diffusers having the same macroscopic parameter. We then show for the first time, to the best of our knowledge, that the trained CNN is able to generalize and make high-quality object predictions through an entirely different set of diffusers of the same class. Our work paves the way to a highly scalable DL approach for imaging through scattering media. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement