Probabilistic Reconstruction of Underwater 3D Acoustic Maps

This paper describes a probabilistic technique for the reconstruction of underwater surfaces in 3D acoustic images. Beamforming is often used to build a range image from backscattered echoes, associated point by point with the intensity information that may represent the confidence of each range measure. In the proposed algorithm, range and confidence images are modelled as Markov Random Fields (MRFs). The energy functional has been designed to fully embed the physics of the acoustic image-formation process and model the a priori knowledge. Optimal (in the maximum a posteriori probability sense) estimates of the reconstructed range image map and the restored confidence image are obtained by minimizing the energy functional by using simulated annealing. Experimental results show the improvement of the processed images over those obtained by other methods performing separate restoration and reconstruction processes.