J
Jun Zhang
Researcher at University of Wisconsin-Madison
Publications - 8
Citations - 497
Jun Zhang is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Markov model & Image restoration. The author has an hindex of 6, co-authored 8 publications receiving 492 citations.
Papers
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Maximum-likelihood parameter estimation for unsupervised stochastic model-based image segmentation
TL;DR: Two solutions are proposed to solve the problem of model parameter estimation from incomplete data: a Monte Carlo scheme and a scheme related to Besag's (1986) iterated conditional mode (ICM) method, both of which make use of Markov random-field modeling assumptions.
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The mean field theory in EM procedures for blind Markov random field image restoration
TL;DR: A Markov random field model-based EM (expectation-maximization) procedure for simultaneously estimating the degradation model and restoring the image is described, and results show that this approach provides good blur estimates and restored images.
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The application of mean field theory to image motion estimation
Jun Zhang,G.G. Hanauer +1 more
TL;DR: The present paper shows how the mean field theory can be applied to MRF model-based motion estimation, and it provides results nearly as good as SA but with much faster convergence.
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A wavelet-based multiresolution statistical model for texture
Jun Zhang,D. Wang,Q.N. Tran +2 more
TL;DR: A multiresolution statistical model, consisting of random fields in wavelet subbands, is proposed for texture, and has produced promising results in texture synthesis experiments.
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The application of the Gibbs-Bogoliubov-Feynman inequality in mean field calculations for Markov random fields
TL;DR: The Gibbs-Bogoliubov-Feynman inequality of statistical mechanics is adopted, with an information-theoretic interpretation, as a general optimization framework for deriving and examining various mean field approximations for Markov random fields (MRF's).