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Incoherent processing for synthetic aperture radar (SAR) is a promisingapproach that enables low implementation costs, simplified hardware designs andoperations in high frequency spectrum compared to the conventional imagingmethods using coherent processing. Existing non-convex phaseless imagingalgorithms offer recovery guarantees over limited range of forward models. Inrecent years, several deep learning (DL) based techniques have been introducedwith the goal of extending applicability of phaseless imaging techniques towave-based imaging modalities by addressing fundamental challenges, such as,lack of redundancy, non-uniqueness issues encountered commonly with inversescattering models. In this paper, we introduce a DL-based phaseless SAR imagingapproach that is designed under the premise that the spectral estimationtechnique, widely used for initializing non-convex phase retrieval algorithms,has significance far beyond generating good initial points. We extend theiterative power method for spectral estimation by using deep denoisers at eachiteration, and subsequently design a deep imaging network within theplug-and-play framework. Finally, we verify the feasibility of our approachusing synthetic SAR measurements.

Deep Denoising Prior-Based Spectral Estimation for Phaseless Synthetic Aperture Radar