ARSAR-Net: Adaptively Regularized SAR Imaging Network with Efficient Unfolding

Abstract

Developed from sparse reconstruction approaches, deep unfolding networks (DUNs) have constituted an emerging method for synthetic aperture radar (SAR) imaging, offering fast convergence and data-driven learning. However, baseline unfolding networks, derived from iterative sparse reconstruction algorithms such as alternating direction method of multipliers (ADMM), lack generalization capability across scenes, as their regularizers are empirically designed and keep unchanged during imaging. In this study, we introduce a learnable regularizer to the unfolding network and propose an adaptively regularized SAR imaging network (ARSAR-Net) for scene-agnostic imaging (imaging across heterogeneous scenes of varying sparsity levels). In practice, the vanilla ARSAR-Net suffers from inherent structural limitations in 2D signal processing, primarily due to its reliance on matrix inversion. To conquer this, we further develop an ADMM without matrix inversion for efficient unfolding, by designing linear operations to replace the time-consuming matrix inversion operations. Experiments upon simulated and real-data demonstrate three advantages of ARSAR-Net: (1) a PSNR gain of up to 2.0 dB in imaging quality compared to existing deep network based imaging methods, (2) enhanced adaptability to complex scenes, and (3) a 50\% increase in imaging speed over existing unfolding networks. These advancements establish a new paradigm for efficient and scene-agnostic SAR imaging systems. Code is available at github.com/ShipenFyu/ARSAR-Net.