Abstract
The framework is presented of Bayesian image restoration for multi-valued images by means of the Q-Ising model. Hyper parameters in the probabilistic model are determined so as to maximize the marginal likelihood. Practical algorithms are described based the conventional mean-field approximation and loopy belief propagation. We compare the results empirically with those provided by conventional filters and the new methods are found to be superior.
Sign-in/Register to access full text options 
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.
