Abstract
AbstractProbability 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
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Imaging Systems and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
