A Probabilistic Approach to the Coupled Reconstruction and Restoration of Underwater Acoustic Images
Issue No. 01 - January (1998 vol. 20)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.655646
<p><b>Abstract</b>—This paper describes a probabilistic technique for the coupled reconstruction and restoration of underwater acoustic images. The technique is founded on the physics of the image-formation process. Beamforming, a method widely applied in acoustic imaging, is used to build a range image from backscattered echoes, associated point by point with another type of information representing the reliability (or confidence) of such an image. Unfortunately, this kind of images is plagued by problems due to the nature of the signal and to the related sensing system. In the proposed algorithm, the range and confidence images are modeled as Markov random fields whose associated probability distributions are specified by a single energy function. This function has been designed to fully embed the physics of the acoustic image-formation process by modeling a priori knowledge of the acoustic system, the considered scene, and the noise-affecting measures and also by integrating reliability information to allow the coupled and simultaneous reconstruction and restoration of both images. 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 function using simulated annealing. Experimental results show the improvement of the processed images over those obtained by other methods performing separate reconstruction and restoration processes that disregard reliability information.</p>
Reconstruction, restoration, Markov random fields, underwater acoustic imaging, beamforming, confidence-based approach, physics-based vision.
Vittorio Murino, Andrea Trucco, Carlo S. Regazzoni, "A Probabilistic Approach to the Coupled Reconstruction and Restoration of Underwater Acoustic Images", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 20, no. , pp. 9-22, January 1998, doi:10.1109/34.655646