Self-Supervised Multibeam Sonar Restoration via Coherent Forward Modeling
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 spre...
| Authors | Zixin Wang, James Hopgood, Mike Davies |
|---|---|
| Primary category | eess.IV |
| Published | 22 October 2026 |
| Last updated at source | 22 October 2026 |
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