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Image deconvolution using hidden Markov tree modeling of complex wavelet packets

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3 Author(s)
Jalobeanu, A. ; CNRS/INRIA/UNSA, INRIA, Sophia Antipolis, France ; Kingsbury, Nick ; Zerubia, J.

In this paper, we propose to use a hidden Markov tree modeling of the complex wavelet packet transform, to capture the inter-scale dependencies of natural images. First, the observed image, blurred and noisy, is deconvolved without regularization. Then its transform is denoised within a Bayesian framework using the proposed model, whose parameters are estimated by an EM technique. The total complexity of this new deblurring algorithm remains O(N)

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Image Processing, 2001. Proceedings. 2001 International Conference on  (Volume:1 )

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