Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalization
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.
Cite
@article{arxiv.2605.24716,
title = {Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalization},
author = {Swapna Pillai and Siddharth Singh Savner and Sujit Kumar Sahoo},
journal= {arXiv preprint arXiv:2605.24716},
year = {2026}
}