论文标题
统计依赖性超出线性相关性超出无序介质散射的光线相关性
Statistical Dependencies Beyond Linear Correlations in Light Scattered by Disordered Media
论文作者
论文摘要
通过散射和随机培养基进行成像是一个出色的问题，即迄今为止通过测量中型传输矩阵或利用传输斑点模式中的线性相关性来解决的问题。但是，传输矩阵技术需要干涉稳定性和线性相关性，例如记忆效应，只能在薄散射介质中利用。在这里，我们显示了在不期望一阶相关性的情况下，在强分散的光场中存在统计依赖性。我们还表明，这种统计依赖性和相关信息传输直接与强烈散射，动态媒体中的人工神经网络成像有关。这些非平凡的依赖性为通过动态和厚厚的散射介质提供了成像的关键，并应用了通过烟雾或雾进行深组织成像或成像
Imaging through scattering and random media is an outstanding problem that to date has been tackled by either measuring the medium transmission matrix or exploiting linear correlations in the transmitted speckle patterns. However, transmission matrix techniques require interferometric stability and linear correlations, such as the memory effect, can be exploited only in thin scattering media. Here we show the existence of a statistical dependency in strongly scattered optical fields in a case where first-order correlations are not expected. We also show that this statistical dependence and the related information transport is directly linked to artificial neural network imaging in strongly scattering, dynamic media. These non-trivial dependencies provide a key to imaging through dynamic and thick scattering media with applications for deep-tissue imaging or imaging through smoke or fog
