用物理约束的自监督方法，提升声呐去斑的泛化能力。
Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalization
- 在对数同态域中将去斑问题转为残差一致性建模。
- 无需干净图像监督，在多个真实数据集上达到最优性能。
- 适合实时部署，对跨数据集变化有强鲁棒性。
本文提出一种物理引导的自监督声呐图像去斑框架，将去斑问题重新定义为对数同态域中的残差一致性。通过约束对数比残差满足乘性斑点统计特性，该方法无需清洁图像监督即可防止退化为恒等映射。结合方差导向的统计损失、边缘感知的结构正则化以及中值引导的课程稳定机制，有效抑制斑点同时保持结构保真度。该方法配合轻量级神经网络，在多个真实声呐数据集上实现当前最佳性能，并表现出优异的跨数据集鲁棒性，同时适合实时部署。
原文摘要 · Abstract (English)
This letter introduces a physics-informed self-supervised framework for sonar image despeckling that reformulates despeckling as residual consistency in the homomorphic log domain. By constraining the log-ratio residual to obey multiplicative speckle statistics, the proposed method eliminates the need for clean supervision while preventing degenerate identity solutions. A variance-targeted statistical loss combined with edge-aware structural regularization and median-guided curriculum stabilization enables effective speckle suppression with preserved structural fidelity. This formulation along with a lightweight neural network achieves state-of-the-art performance across multiple real sonar datasets and demonstrates excellent cross-dataset robustness, while remaining suitable for real-time deployment.
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