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arXiv · 2605.24716

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

Swapna Pillai·Siddharth Singh Savner·Sujit Kumar Sahoo
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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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Swapna Pillai, Siddharth Singh Savner, Sujit Kumar Sahoo. 2026-05-23. Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalization. https://doi.org/10.1109/lsp.2026.3697693

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