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Conference Papers Year : 2013

Generalized multivariate exponential power prior for wavelet-based multichannel image restoration

Abstract

In multichannel imaging, several observations of the same scene acquired in different spectral ranges are available. Very often, the spectral components are degraded by a blur modelled by a linear operator and an additive noise. In this paper, we address the problem of recovering the image components in a wavelet domain by adopting a variational approach. Our contribution is twofold. First, an appropriate multivariate penalty function is derived from a novel joint prior model of the probability distribution of the wavelet coefficients located at the same spatial position in a given subband through all the channels. Secondly, we address the challenging issue of computing the Maximum A Posteriori estimate by using a Majorize-Minimize optimization strategy. Simulation tests carried out on multispectral satellite images show that the proposed method outperforms conventional techniques.
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Dates and versions

hal-00829444 , version 1 (03-06-2013)

Identifiers

  • HAL Id : hal-00829444 , version 1

Cite

Yosra Marnissi, Amel Benazza-Benyahia, Emilie Chouzenoux, Jean-Christophe Pesquet. Generalized multivariate exponential power prior for wavelet-based multichannel image restoration. 20th IEEE International Conference on Image Processing (ICIP 2013), Sep 2013, Melbourne, Australia. pp.2402-2406. ⟨hal-00829444⟩
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