Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalization

• URL: http://arxiv.org/abs/2605.24716v1
• Date: Sat, 23 May 2026 20:07:49 GMT
• Title: Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalization
• Abstract summary: This letter introduces a physics-informed self-supervised framework for sonar image despeckling that reformulates despeckling as residual consistency in the homomorphic log domain.<n>A variance-targeted statistical loss combined with edge-aware structural regularization and median-guided curriculum stabilization enables effective speckle suppression with preserved structural fidelity.
• Score: 1.9371675344367494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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