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Markov‐based methodology for the restoration of underwater acoustic images

This article describes a probabilistic technique for the restoration of 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 three‐dimensional (3D) map, that is associated point by point with the estimates of the reliability of such measures. Specifically, backscattered echoes that are received by a 2D array antenna are arranged to generate two images in which each pixel represents the distance (range) from 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 proposed algorithm, the range and the confidence images are modeled as separate MRFs whose associated probability distributions embed knowledge of the acoustic system, of the considered scene, and of the noise affecting the measures. In particular, the confidence image is first restored and the result is used to reconstruct the 3D image to allow an active integration of the reliability information. Optimal (in the maximum a posteriori probability sense) estimates of the reconstructed 3D map and the restored confidence image are obtained by minimizing the energy functionals, using simulated annealing. Experimental results on synthetic and real images show the performance of the proposed approach. © 1997 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 8, 386–395, 1997