GPLFR—Global perspective and local flow registration-for forward-looking sonar images

Forward-looking sonar (FLS) image registration is a key step in many underwater applications such as underwater target detection, ocean observation, and mapping. However, low resolution, low signal-to-noise ratio, and the complex nonlinear transformation relationship between FLS images from two different viewpoints have brought great challenges to register them. In order to better cope with this challenge, we propose a global perspective and local flow registration (GPLFR) method for FLS images. GPLFR consists of two networks, i.e., a regression correction network (RCNet) and a deformable network (IRRDNet) with the iterative refinement of the residual. For a given pair of FLS images, RCNet is used to estimate the global transformation parameters to achieve global registration, and then, IRRDNet is used to estimate the deformation field or flow field to realize local alignment. The experimental results on real FLS image and 2D face expression image registration tasks demonstrate the effectiveness and robustness of the proposed method.