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Bayesian inference for inverse problems in signal and image processing and applications
Author(s) -
MohammadDjafari Ali
Publication year - 2006
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.20081
Subject(s) - computer science , deconvolution , inverse problem , bayesian probability , signal (programming language) , signal processing , artificial intelligence , image processing , image (mathematics) , bayesian inference , inference , focus (optics) , blind deconvolution , algorithm , computer vision , pattern recognition (psychology) , mathematics , digital signal processing , computer hardware , optics , programming language , mathematical analysis , physics
Probability theory and statistics are two main tools in signal and image processing. Bayesian inference has a privileged place in developing methods for inverse problems arising in signal and image processing, which can be applied in real world applications. In this tutorial presentation, first I will briefly present the Bayesian estimation approach in signal and image processing. Then, I will show a few examples of inverse problems, such as signal deconvolution, image restoration, and tomographic image construction, and then show how the Bayesian estimation approach can be used to give solutions for these problems. Finally, I will focus on two recent research domain, which are blind sources separation and data fusion problems, and present new methods we developed recently and their applications. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 16, 209–214, 2006