Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors

@article{BioucasDias2006BayesianWI,
  title={Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors},
  author={Josx00E9 M. Bioucas-Dias},
  journal={IEEE Transactions on Image Processing},
  year={2006},
  volume={15},
  pages={937-951}
}
Image deconvolution is formulated in the wavelet domain under the Bayesian framework. The well-known sparsity of the wavelet coefficients of real-world images is modeled by heavy-tailed priors belonging to the Gaussian scale mixture (GSM) class; i.e., priors given by a linear (finite of infinite) combination of Gaussian densities. This class includes, among others, the generalized Gaussian, the Jeffreys , and the Gaussian mixture priors. Necessary and sufficient conditions are stated under… CONTINUE READING
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