MARKOV-BASED METHODOLOGY FOR THE RESTORATION OF UNDERWATER ACOUSTIC IMAGES

This article describes a probabilistic technique for the restoration o f underwater acoustic images that is based on the Markov random fields (MRFs) methodology. The beamforming is applied to rough acoustic data that derive from multibeam systems or acoustic cameras to build a thr ee-dimensional (3D) map, that is associated point by point with the es timates of the reliability of such measures, Specifically, backscatter ed echoes that are received by a 2D array antenna are arranged to gene rate two images in which each pixel represents the distance (range) fr om the sensor plane and the confidence of the measures, respectively. Unfortunately, this kind of image is affected by several problems due to the nature of the signal and the related sensing system. In the pro posed algorithm, the range and the confidence images are modeled as se parate MRFs whose associated probability distributions embed knowledge of the acoustic system, of the considered scene, and of the noise aff ecting the measures. In particular, the confidence image is first rest ored and the result is used to reconstruct the 3D image to allow an ac tive integration of the reliability information. Optimal (in the maxim um a posteriori probability sense) estimates of the reconstructed 3D m ap and the restored confidence image are obtained by minimizing the en ergy functionals, using simulated annealing. Experimental results on s ynthetic and real images show the performance of the proposed approach . (C) 1997 John Wiley & Sons, Inc.