Synthetic aperture radar (SAR) provides valuable information about the Earth's surface under all weather and illumination conditions. However, the inherent phenomenon of speckle and the presence of sidelobes around bright targets pose challenges for accurate interpretation of SAR imagery. Most existing SAR image restoration methods address despeckling and sidelobes reduction as separate tasks. In this paper, we propose a unified framework that jointly performs both tasks using neural networks (NNs) trained on a realistic SAR simulated dataset generated with MOCEM. Inference can then be performed on real SAR images, demonstrating effective simulation to real (Sim2Real) transferability. Additionally, we incorporate acquisition metadata as auxiliary input to the NNs, demonstrating improved restoration performance.

翻译：合成孔径雷达（SAR）能够在全天候与全光照条件下提供地球表面的宝贵信息。然而，固有的斑点噪声现象以及明亮目标周围的旁瓣存在，对SAR图像的精确解译构成了挑战。现有的大多数SAR图像复原方法将去斑与旁瓣抑制作为独立任务进行处理。本文提出了一种统一框架，利用在MOCEM生成的逼真SAR仿真数据集上训练的神经网络（NNs），联合执行这两项任务。随后可在真实SAR图像上进行推理，证明了从仿真到真实（Sim2Real）的有效迁移性。此外，我们将采集元数据作为神经网络的辅助输入，展示了其能提升复原性能。