SAR Super-Resolution Imaging Incorporating Nonconvex Regularizations

In synthetic aperture radar (SAR) imaging, the inherent system configuration often limits spatial resolution, while noise further degrades structural information. Sparse priors have been widely adopted for super-resolution reconstruction, yet they often fail to preserve regional structures. To overcome these limitations, we propose a super-resolution algorithm that combines a Cauchy-based penalty with minimax concave graph total variation (MC-GTV) regularization. The non-convex Cauchy-based penalty alleviates estimation bias and enhances robustness, while the MC-GTV regularization, also non-convex, highlights fine structural details and maintains regional smoothness through a neighborhood-graph model. A primal–dual proximal algorithm is employed to ensure stable convergence. Experimental results on simulated point targets and real SAR data demonstrate that the proposed method effectively suppresses sidelobes, improves resolution, and preserves fine structural compared with convex regularization approaches.