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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Estimation for Range Image Segmentation and Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
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EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
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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.