Object classification through unseen scatters with a physical model trained neural network

Abstract

Deep learning (DL) has been extensively employed to imaging or classify objects through scattering media. However, the performance of a trained network degrades dramatically when it is tested at scattering conditions statistically differing from those in the training. Therefore, how to retain DL’s generalization capacity is still a challenging task, with imaging or classification through untrained (unseen) scatters is highly desired. Here, we introduce physical model approach for network training, which enables generalization capability via learning from sufficiently large amount of model-based synthetic speckles. By mimicking the point spread function (PSF) of an incoherent scattering system, and diversifying the scattering realizations with random phasers, a generalized neural network is obtained. Object classification through unseen scatters is experimentally demonstrated. The random phasers prohibit DL from converging to a specific training medium, which is crucial to the creation of a generalized neural network. Unlike traditional data-driven DL that has nearly zero predicting accuracy, our model-trained network is generalized and being able to classify objects through untrained scattering media with accuracy as high as 60%. Neither the experimental scenes nor system geometry is required in creating the synthetic speckles, our method represents a genuine approach in data generation for DL training, paving the way to a highly generalized neural network for inverse scattering problems.