JOINT TRACE/TV NORM MINIMIZATION: A NEW EFFICIENT APPROACH FOR SPECTRAL COMPRESSIVE IMAGING

Abstract : In this paper we propose a novel and efficient model for compressed sensing of hyperspectral images. A large-size hyperspectral image can be subsampled by retaining only 3\% of its original size, yet robustly recovered using the new approach we present here. Our reconstruction approach is based on minimizing a convex functional which penalizes both the trace norm and the TV norm of the data matrix. Thus, the solution tends to have a simultaneous low-rank and piecewise smooth structure: the two important priors explaining the underlying correlation structure of such data. Through simulations we will show our approach significantly enhances the conventional compression rate-distortion tradeoffs. In particular, in the strong undersampling regimes our method outperforms the standard TV denoising image recovery scheme by more than 17dB in the reconstruction MSE.
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Communication dans un congrès
IEEE International Conference on Image Processing (ICIP 2012), Sep 2012, Orlando, Florida, United States. 2012
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https://hal.inria.fr/hal-00705752
Contributeur : Jules Espiau de Lamaestre <>
Soumis le : vendredi 8 juin 2012 - 11:07:36
Dernière modification le : lundi 2 octobre 2017 - 16:06:02

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  • HAL Id : hal-00705752, version 1

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Mohammad Golbabaee, Pierre Vandergheynst. JOINT TRACE/TV NORM MINIMIZATION: A NEW EFFICIENT APPROACH FOR SPECTRAL COMPRESSIVE IMAGING. IEEE International Conference on Image Processing (ICIP 2012), Sep 2012, Orlando, Florida, United States. 2012. 〈hal-00705752〉

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