Bayesian image and pattern reconstruction from incomplete and noisy data

Authors:
V. L. Vengrinovich
Affiliations:
Institute of Applied Physics, National Academy of Sciences, Minsk, Belarus
Venue:
Pattern Recognition and Image Analysis
Year:
2012

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IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

A Bayesian iterative method can be the basis for a wide range of technologies in the field of pattern recognition and image reconstruction. It involves finding the most probable solutions for images or patterns, if functionals describing the likelihood function and a priori information, respectively, are already known. The article describes the basic principles and recent advances in the development of BIM and its applications in various fields, mainly in tomography and restoration of functions from incomplete and noisy data.