PAFM-GAN: Physics-Aware and Frequency-Regularized GAN for SAR-to-Optical Image Translation

SAR-to-optical image translation (S2OIT) aims to transform the complex backscattering characteristics of Synthetic Aperture Radar (SAR) into more interpretable optical appearances. However, existing methods often suffer from over-smoothed structural details, generation of pseudo-textures caused by inconsistencies between generated textures and real optical images, and insufficient global consistency in complex scenes. To address these challenges, we propose a Physics-Aware and Frequency-Regularized Generative Adversarial Network (PAFM-GAN) for SAR-to-optical translation. Specifically, we extract local statistical and edge structural cues from SAR images and inject them into the generator as additional guidance, which enhances structural authenticity and mitigates the impact of speckle noise. To mitigate spectral misalignment and suppress high-frequency artifacts, we further transform both the generated and real optical images into the Fourier frequency domain and perform spectral distribution alignment between them. We also introduce a frequency-domain discriminator to suppress unrealistic high-frequency components, thereby effectively reducing spurious details in the synthesized results. In addition, to capture long-range dependencies under high-resolution scenarios with low computational overhead, we integrate a Mamba-based state space module (SSM) into the generator for efficient global context modeling, improving scene-level style coherence and overall consistency. Extensive experiments on the SAR2Opt, SEN1-2, and QXS-SAROPT demonstrate that PAFM-GAN consistently outperforms representative SAR-to-optical baselines across five metrics, including PSNR, SSIM, FID, LPIPS, and FSIMc.  In addition, the results of multiple ablation experiments validate the effectiveness of the proposed method.