通过对数域变换，用生成模型高效去除雷达图像斑点噪声。
Self-Supervised Score-Based Despeckling for SAR Imagery via Log-Domain Transformation
- 在对数域中将乘性斑点转为加性高斯噪声，适配得分生成模型。
- 自监督训练无需干净图像，推理速度显著快于现有方法。
- 适合遥感图像处理、雷达数据修复等实际应用需求。
合成孔径雷达（SAR）图像固有的斑点噪声严重降低图像质量并影响后续分析。由于SAR斑点为乘性且服从伽马分布，有效去噪仍具挑战。本文提出一种基于得分生成模型的新型自监督框架，该框架在变换后的对数域中进行操作。首先将数据转换至对数域，使斑点噪声残差近似为加性高斯分布，从而可应用得分模型。模型在变换域中使用自监督目标训练，通过输入数据自身的进一步污染版本学习真实信号。因此，该方法相比许多现有自监督技术具有显著更短的推理时间，提供了一种鲁棒且实用的SAR图像复原方案。
原文摘要 · Abstract (English)
The speckle noise inherent in Synthetic Aperture Radar (SAR) imagery significantly degrades image quality and complicates subsequent analysis. Given that SAR speckle is multiplicative and Gamma-distributed, effectively despeckling SAR imagery remains challenging. This paper introduces a novel self-supervised framework for SAR image despeckling based on score-based generative models operating in the transformed log domain. We first transform the data into the log-domain and then convert the speckle noise residuals into an approximately additive Gaussian distribution. This step enables the application of score-based models, which are trained in the transformed domain using a self-supervised objective. This objective allows our model to learn the clean underlying signal by training on further corrupted versions of the input data itself. Consequently, our method exhibits significantly shorter inference times compared to many existing self-supervised techniques, offering a robust and practical solution for SAR image restoration.
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