用模拟数据训练神经网络，同时去除雷达图像的斑点和旁瓣。
Sim2Real SAR Image Restoration: Metadata-Driven Models for Joint Despeckling and Sidelobes Reduction
- 统一框架联合处理斑点抑制与旁瓣消除，提升恢复效果。
- 利用真实雷达数据验证，实现从仿真到现实的有效迁移。
- 加入成像元数据辅助输入，进一步提升修复质量。
合成孔径雷达（SAR）可在全天候、全时段条件下提供地表信息，但固有的斑点噪声以及强目标周围的旁瓣现象给图像准确解译带来挑战。现有大多数SAR图像复原方法将去斑和旁瓣抑制视为独立任务。本文提出一种统一框架，通过在使用MOCEM生成的真实感SAR模拟数据集上训练神经网络，实现对真实SAR图像的联合去斑与旁瓣抑制。该方法具备良好的模拟到现实（Sim2Real）迁移能力。此外，我们引入成像元数据作为神经网络的辅助输入，显著提升了复原性能。
原文摘要 · Abstract (English)
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.
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