Coarse-to-Fine Nonrigid Registration for Side-Scan Sonar Mosaicking

Abstract

Side-scan sonar (SSS) mosaicking plays a crucial role in large-scale seabed mapping but is challenged by complex nonlinear, spatially varying distortions due to diverse sonar acquisition conditions. Existing rigid or affine registration methods fail to model such complex deformations, whereas traditional nonrigid techniques tend to overfit and lack robustness in sparse-texture sonar data. To address these challenges, we propose a coarse-to-fine hierarchical nonrigid registration framework tailored for large-scale SSS images. Our method begins with a global thin plate spline (TPS) initialization from sparse correspondences, followed by superpixel-guided segmentation that partitions the image into structurally consistent patches preserving terrain integrity. Each patch is then refined by a pretrained SynthMorph network in an unsupervised manner, enabling dense and flexible alignment without task-specific training. Finally, a fusion strategy integrates both global and local deformations into a smooth, unified deformation field. Extensive quantitative and visual evaluations demonstrate that our approach significantly outperforms state-of-the-art rigid, classical nonrigid, and learning-based methods in accuracy, structural consistency, and deformation smoothness on the challenging sonar dataset.