Deep Learning based Synthetic Aperture Imaging in the Presence of Phase Errors via Decoding Priors

Abstract

In this paper, we designed a deep learning (DL) based method for synthetic aperture imaging in the presence of phase errors. Random variations in the transmission medium resulting from unforeseen environmental changes, fluctuations in sensor locations, and multiple scattering effects in the background medium often amount to uncertainties in the assumed data models. Imaging algorithms that rely on back-projected estimates are susceptible to estimation errors under these circumstances. Moreover, under dynamic nature of the medium, collecting high volume of measurements under the same operating conditions may become challenging. Towards this end, our imaging network incorporates DL in three major steps: first, we implement a deep network (DN) for pre-processing the erroneous measurements; second, we implement a DL-based decoding prior by recovering an encoded version of the reflectivity vector associated with the scattering media to reduce sample complexity, which is then mapped to an image estimate by a decoding DN; finally, we consider a fixed step implementation of an iterative algorithm in the form of a recurrent neural network (RNN) by using the unrolling technique that leads to a model-based imaging operator. The parameters of all three DNs are learned simultaneously in a supervised manner. We verified the feasibility of our approach using simulated high fidelity synthetic aperture measurements.