Fast Adaptive Plug-and-Play ADMM Framework for Short-Range 3-D SAR Imaging

Abstract

The advancement of short-range millimeter-wave (MMW) synthetic aperture radar (SAR) imaging has shown significant advancements in various fields, including security surveillance, non-destructive evaluation, and medical diagnostics. This paper presents a fast adaptive plug-and-play alternating direction method of multipliers (FA-PnP-ADMM) framework designed to improve the efficiency and accuracy of SAR image reconstruction. By addressing key challenges like image degradation caused by fast Fourier transform (FFT) operations and the computational burden of conventional ADMM methods, our framework significantly improves performance. Concretely, alongside a PnP strategy, the proposed FA-PnP-ADMM framework leverages the state-of-the-art single-frequency holographic (SFH) ADMM-based image-solving model and the adaptive parameter adjustment predicated on the relationship between relaxed ADMM and relaxed Douglas-Rachford splitting (DRS). This innovative integration significantly accelerates convergence and reduces computational overhead. Furthermore, the methodology incorporates proficient denoising deep learning (DL) architectures, encompassing convolutional neural network (CNN) and auto-encoder (AE), seamlessly embedded within the iterative process, resulting in a tailored PnP-DL-ADMM. This synergy not only enhances noise suppression and image fidelity but also adapts effectively to diverse scene complexities and noise levels. Unlike previous works that employ these techniques separately, our approach integrates adaptive optimization and DL-based denoisers into a unified framework optimized for short-range 3D SAR imaging. Experimental results demonstrate substantial improvements in both runtime and reconstruction quality, highlighting the practicality and impact of this methodology for real-world applications.