Skip to Main content Skip to Navigation
Conference papers

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.
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-00829444
Contributor : Emilie Chouzenoux <>
Submitted on : Monday, June 3, 2013 - 12:18:48 PM
Last modification on : Tuesday, June 16, 2020 - 11:28:03 AM

Identifiers

  • HAL Id : hal-00829444, version 1

Citation

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⟩

Share

Metrics

Record views

384