Satellite images of synthetic aperture radar (SAR) sensors are contaminated by speckles from the coherent imaging mechanism. Although removing or mitigating speckle has been a critical issue for SAR applications, effective reduction continues to be a significant challenge for existing methods when preserving the intricate structures within SAR images. To address this issue, this work proposes a novel conditional diffusion model for SAR despeckling (DiffusionSAR). The new method explicitly learns data distributions by forward diffusion toward multiplicative gamma noise. The logarithmic and Yeo–Johnson (log-Yeo–Johnson) transformation are harnessed in preprocessing for fine-tuning or hybrid training. A prolonging steps technique is suggested in fine-tuning to match the preprocessing. A new synthetic dataset is designed for satellite SAR despeckling. The proposed method is compared with eight state-of-the-art methods using both synthetic and real-world SAR satellite images. The qualitative and quantitative evaluations confirm the effectiveness of the proposed method in structural preservation as well as noise reduction. A fine-tuning experiment using stacked multitemporal data shows the necessity of tine-tuning training in bridging the domain gap when trained with synthetic data and tested with real-world SAR data.

中文翻译：

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使用 Log-Yeo__ohnson 变换和条件扩散模型去斑 SAR 图像


合成孔径雷达 (SAR) 传感器的卫星图像受到相干成像机制产生的散斑的污染。尽管去除或减轻散斑一直是 SAR 应用的关键问题，但在保留 SAR 图像中的复杂结构时，有效减少散斑仍然是现有方法的重大挑战。为了解决这个问题，本文提出了一种新颖的 SAR 去斑条件扩散模型 (DiffusionSAR)。新方法通过向乘性伽马噪声前向扩散来明确学习数据分布。在预处理中利用对数和 Yeo-Johnson (log-Yeo-Johnson) 变换进行微调或混合训练。建议在微调中使用延长步骤技术以匹配预处理。一个新的合成数据集是为卫星 SAR 去斑而设计的。所提出的方法与使用合成和真实 SAR 卫星图像的八种最先进的方法进行了比较。定性和定量评估证实了该方法在结构保护和降噪方面的有效性。使用堆叠多时相数据的微调实验表明，在使用合成数据进行训练并使用真实 SAR 数据进行测试时，微调训练有必要弥合域差距。