In recent years, diffusion models (DMs) have become a popular method for generating synthetic data. By achieving samples of higher quality, they quickly became superior to generative adversarial networks (GANs) and the current state-of-the-art method in generative modeling. However, their potential has not yet been exploited in radar, where the lack of available training data is a long-standing problem. In this work, a specific type of DMs, namely denoising diffusion probabilistic model (DDPM) is adapted to the SAR domain. We investigate the network choice and specific diffusion parameters for conditional and unconditional SAR image generation. In our experiments, we show that DDPM qualitatively and quantitatively outperforms state-of-the-art GAN-based methods for SAR image generation. Finally, we show that DDPM profits from pretraining on largescale clutter data, generating SAR images of even higher quality.

翻译：近年来，扩散模型（DMs）已成为生成合成数据的主流方法。通过生成更高质量的样本，它们迅速超越生成对抗网络（GANs），成为当前生成建模领域的最先进技术。然而，在训练数据匮乏这一长期问题的雷达领域，其潜力尚未得到开发。本研究将一种特定类型的扩散模型——去噪扩散概率模型（DDPM）——引入SAR领域。我们探究了用于条件与非条件SAR图像生成的网络选择及特定扩散参数。实验表明，DDPM在定性和定量上均优于基于GAN的SAR图像生成方法。最后，我们证明DDPM可通过大规模杂波数据的预训练受益，生成质量更高的SAR图像。