Spectral Design in Markov Random Fields
Abstract
Markov random fields (MRFs) have been shown to be a powerful and relatively compact stochastic model for imagery in the context of Bayesian estimation. The simplicity of their conventional embodiment implies local computation in iterative processes and relatively noncommittal statistical descriptions of image ensembles, resulting in stable estimators, particularly under models with strictly convex potential functions. This simplicity may be a liability, however, when the inherent bias of minimum mean-squared error or maximum a posteriori probability (MAP) estimators attenuate all but the lowest spatial frequencies. In this paper we explore generalization of MRFs by considering frequency-domain design of weighting coefficients which describe strengths of interconnections between clique members.
- Publication:
-
Bayesian Inference and Maximum Entropy Methods in Science and Engineering
- Pub Date:
- March 2011
- DOI:
- 10.1063/1.3573652
- Bibcode:
- 2011AIPC.1305..451W
- Keywords:
-
- inverse problems;
- functional analysis;
- Markov processes;
- matrix algebra;
- 02.30.Zz;
- 02.30.Sa;
- 02.50.Ga;
- 02.10.Yn;
- Inverse problems;
- Functional analysis;
- Markov processes;
- Matrix theory