A generalized Gaussian image model for edge-preserving MAP estimation

  title={A generalized Gaussian image model for edge-preserving MAP estimation},
  author={Charles A. Bouman and Ken D. Sauer},
  journal={IEEE transactions on image processing : a publication of the IEEE Signal Processing Society},
  volume={2 3},
The authors present a Markov random field model which allows realistic edge modeling while providing stable maximum a posterior (MAP) solutions. The model, referred to as a generalized Gaussian Markov random field (GGMRF), is named for its similarity to the generalized Gaussian distribution used in robust detection and estimation. The model satisfies several desirable analytical and computational properties for map estimation, including continuous dependence of the estimate on the data… CONTINUE READING
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