Abstract
This paper introduces bilateral Markov mesh random field to overcome the shortcomings of the conventional Markov random fields in image modeling. These shortcomings consist of (a) the computational intractability of such fields when expressing the image probability function in the form of the Gibbs distribution function, and (b) the formulation of the image probability function via the product of low-dimensional densities at the expense of obtaining non-symmetrical image models. The properties of bilateral Markov mesh random field are presented and used to derive an image model to address the above shortcomings. As an application, a framework for image restoration is then provided. Restoration results based on this new bilateral Markov mesh random field are compared to the conventional fields to demonstrate its effectiveness.
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: Journal of Visual Communication and Image Representation
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.
