Abstract
We propose a joint estimation of the parameters and hyperparameters (the parameters of the prior law) when a Bayesian approach with maximum entropy (ME) priors is used to solve the inverse problems which arise in signal and image reconstruction and restoration problems. In particular we propose two methods: one based on the expectation maximization (EM) algorithm who aims to find the marginalized MAP (MMAP) estimate and the second based on a joint MAP estimation (JMAP). We discuss and compare these methods and give some simulation results in image restoration to show the relative performances of the proposed methods.
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 callDisclaimer: 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.
