Newest papers

Self-Supervised Multibeam Sonar Restoration via Coherent Forward Modeling

Zixin Wang, James Hopgood, Mike Davies · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31490 · cited by 0 works (OpenAlex) · open-access PDF

Spanish

El sonar de imagen multibeam favorito produce imágenes de intensidad degradadas por especulaciones coherentes e inducidas por el sistema, mientras que los objetivos de reflexión limpia son raramente disponibles. Los métodos de despejado existentes a menudo se basan en modelos de ruido de intensidad simplificados de dominio, objetivos sintéticos limpios o autocontrol de puntos ciegos, y por lo general no modelan conjuntamente el despejado y la supresión de especulaciones. Proponemos un marco de auto supervisión físico-consistente para el despejado y despejado conjunto de sonar de imagen multibeam. Un modelo de imagen coherente estocástico produce un operador frontal condicionado que relaciona la reflexión de la escena latente con la intensidad esperada a través de la magnitud cuadrada de la

Machine translation (Meta M2M100); the English original follows.

Abstract

Multibeam imaging sonar produces intensity images degraded by coherent speckle and system-induced blur, while clean reflectivity targets are rarely available. Existing despeckling methods often rely on simplified intensity-domain noise models, synthetic clean targets, or blind-spot self-supervision, and typically do not jointly model deblurring and speckle suppression. We propose a physics-consistent self-supervised framework for joint despeckling and deblurring of multibeam imaging sonar. A stochastic coherent image-formation model yields a conditional-mean forward operator that relates latent scene reflectivity to expected intensity through the squared magnitude of the system point spread function. We then use registered multi-frame sonar sequences to train without clean ground truth images, showing that, under an idealized inter-frame conditional-mean condition, the expected paired loss equals the corresponding forward-prediction loss up to a network-independent constant. To stabilize the ill-conditioned inverse problem, we further adapt robust equivariant imaging using stochastic coherent simulation and introduce a paired forward prediction error metric for ground-truth-free evaluation. Experiments on two real multibeam sonar datasets demonstrate a favourable balance between forward-prediction agreement, homogeneous-region speckle suppression, and preservation of visible structural boundaries compared with representative baselines.

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Topics: Deblurring, Sonar, Speckle noise, Speckle pattern, Computer science, Artificial intelligence

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