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

German

Multibeam imaging sonar produziert intensive bilder, die durch kohärente speckle und systeminduzierte blur degradiert werden, während saubere reflektivitätsziele selten verfügbar sind. Bestehende reflektierungsmethoden verlassen sich oft auf vereinfachte intensitäts-domäne-geräuschmodelle, synthetische saubere ziele oder blind-spot-selbstüberwachung und generieren typischerweise kein gemeinsames model deblurring und speckle-unterdrückung. wir schlagen ein physikalisch kohärentes selbstüberwachendes framework für die gemeinsame deflation und deblurring von multibeam imaging sonar vor. Ein stochastisches kohärentes bild-bild-modell liefert einen bedingten vorausschauenden operator, der die latente scene-ref

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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