PnP-MFAMP-Net: A Novel Plug-and-Play Sparse Reconstruction Network for SAR Imaging

Currently, the sparse imaging problem of synthetic aperture radar (SAR) is primarily addressed by compressed sensing (CS) theory, which introduces prior information into image restoration tasks via regularization. However, simple regularization constraints cannot provide the complex structural information of targets, and their denoising performance is unsatisfactory at low signal-to-noise ratios (SNRs) and sampling rates. In this letter, a novel sparse SAR reconstruction network is proposed based on plug-and-play (PnP) and approximated observation. First, a chirp-scaling algorithm (CSA) operator-derived approximated observation model is applied to reduce computational costs. Then, this sparse imaging problem is iteratively solved with a matched filter-based approximate message-passing (MFAMP) method. To overcome the limitations of prior models in existing sparse imaging methods, a PnP prior model is incorporated within the sparse reconstruction framework instead of using the ℓ 1 sparse regularizer. Finally, the solution procedure is unfolded as a deep imaging network, dubbed as PnP-MFAMP-Net. Experimental results validate its robustness and superiority. Even at a sampling rate of 25%, PnP-MFAMP-Net can achieve a PSNR gain of approximately 15 dB compared to the AMP-Net.