Abstract
The paper introduces modelling and optimization contributions on a class of Bayesian wavelet-based image deconvolution problems. Main assumptions of this class are: 1) space-invariant blur and additive white Gaussian noise; 2) prior given by a linear (finite of infinite) decomposition of Gaussian densities. Many heavy-tailed priors on wavelet coefficients of natural images admit this decomposition. To compute the maximum a posteriori (MAP) estimate, we propose a generalized expectation maximization (GEM) algorithm where the missing variables are the Gaussian modes. The maximization step of the EM algorithm is approximated by a stationary second order iterative method. The result is a GEM algorithm of
computational complexity. In comparison with state-of-the-art methods, the proposed algorithm either outperforms or equals them, with low computational complexity.
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Bioucas-Dias, J.M. (2003). A Fast GEM Algorithm for Bayesian Wavelet-Based Image Restoration Using a Class of Heavy-Tailed Priors. In: Rangarajan, A., Figueiredo, M., Zerubia, J. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2003. Lecture Notes in Computer Science, vol 2683. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45063-4_26
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DOI: https://doi.org/10.1007/978-3-540-45063-4_26
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