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.

中文翻译：

---

深度斑点关联：通过散射介质实现可扩展成像的深度学习方法

通过散射成像是重要但具有挑战性的问题。通过利用固定介质的确定性输入-输出“传输矩阵”，已经取得了巨大的进步。但是，这种“一对一”映射非常容易出现斑点去相关-对散射介质的小扰动会导致模型误差和成像性能严重下降。我们的目标是开发一种新的框架，该框架可高度扩展以适应介质扰动和测量需求。为此，我们提出了一种统计“一对全”深度学习（DL）技术，该技术封装了广泛的统计变化，以使模型具有对斑点去相关的弹性。具体来说，我们开发了一个卷积神经网络（CNN），它能够学习包含在具有相同宏观参数的一组扩散器上捕获的斑点强度模式中所包含的统计信息。然后，据我们所知，这是我们第一次证明受过训练的CNN能够通过一组完全不同的相同类别的扩散器来进行概括和高质量的物体预测。我们的工作为通过散射介质成像的高度可扩展的DL方法铺平了道路。