Wavelet-based Image Deconvolution and Reconstruction

Abstract : Image deconvolution and reconstruction are inverse problems which are encountered in a wide array of applications. Due to the ill-posedness of such problems, their resolution generally relies on the incorporation of prior information through regularizations, which may be formulated in the original data space or through a suitable linear representation. In this article, we show the benefits which can be drawn from frame representations, such as wavelet transforms. We present an overview of recovery methods based on these representations: (i) variational formulations and non-smooth convex optimization strategies, (ii) Bayesian approaches, especially Monte Carlo Markov Chain methods and variational Bayesian approximation techniques, and (iii) Stein-based approaches. A brief introduction to blind deconvolution is also provided.
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Article dans une revue
Wiley Encyclopedia of Electrical and Electronics Engineering, 2016
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Contributeur : Nelly Pustelnik <>
Soumis le : vendredi 21 octobre 2016 - 15:10:49
Dernière modification le : mercredi 15 février 2017 - 13:52:12

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Nelly Pustelnik, Amel Benazza-Benhayia, Yuling Zheng, Jean-Christophe Pesquet. Wavelet-based Image Deconvolution and Reconstruction. Wiley Encyclopedia of Electrical and Electronics Engineering, 2016. <hal-01164833v4>

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