Integrating Regularization and PnP Priors for SAR Image Reconstruction Using Multiagent Consensus Equilibrium

Abstract

The multiagent consensus equilibrium (MACE) mechanism, which generalizes the popular method plug-and-play (PnP)-alternating direction method of multipliers (ADMM) and composite regularization in computational sensing, possesses the notable capacity to incorporate multiple priors aspect of both regularization and PnP for improving image quality. In this work, a flexible synthetic aperture radar (SAR) image reconstruction method based on MACE is proposed to integrate multiple regularization and PnP priors for various features enhancement. The partial-update approach and Mann iteration methods are implemented to increase the computational efficiency of the MACE-based SAR image reconstruction algorithm. A thorough analysis of the proposed algorithm’s convergence and computational complexity is provided. High-quality SAR images necessitate low ambiguity, high target-to-background ratio (TBR), and low coherent speckle. We therefore demonstratively integrate regularization and PnP priors for azimuth ambiguity suppression, sparsity inducing, multiple features enhancement, and despeckling. The proposed method’s performance is evaluated through experiments on both simulated and QILU-1 satellite SAR data.