title: Wavelet-based Bayesian image estimation: from marginal and bivariate prior models to multivariate prior models.
abstract: Prior models play an important role in the wavelet-based Bayesian image estimation problem. Although it is well known that a residual dependency structure always remains among natural image wavelet coefficients, only few multivariate prior models with a closed parametric form are available in the literature. In this paper, we develop new multivariate prior models that not only match well with the observed statistics of the wavelet coefficients of natural images, but also have a simple parametric form. These prior models are very effective for Bayesian image estimation and lead to an improved estimation performance over related earlier techniques.
authors.affiliation: Institute of Intelligent Information Processing, Xidian University, Xi'an, China. tanshan5989@yahoo.com.cn
authors.forename: S
authors.initials: S
authors.lastname: Tan
authors.forename: L
authors.initials: L
authors.lastname: Jiao
authors.forename: I A
authors.initials: IA
authors.lastname: Kakadiaris
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D015999:Multivariate Analysis; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique