Deep Learning Ghost Polarimetry

Abstract

The first application of neural networks in the problem of ghost polarimetry is reported. The proposed approach has enabled the reconstruction of the spatial distribution of object anisotropy in ghost polarimetry. The deep neural network processes a set of intensity correlation functions measured in various polarization states of classical light and reconstructs, point-by-point, the distribution of the type of anisotropy. In this work we use a numerical dataset. We investigated the applicability of the developed network for objects whose properties are determined by linear/circular amplitude/phase anisotropy. The probability of correctly predicting the type of anisotropy exceeds 95% according to the F1-score metric.