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Sharpening Hyperspectral Images Using Spatial and Spectral Priors in a Plug-and-Play Algorithm

  • Afonso M. Teodoro
  • José M. Bioucas-Dias
  • Mário A. T. FigueiredoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10746)

Abstract

This paper proposes using both spatial and spectral regularizers/priors for hyperspectral image sharpening. Leveraging the recent plug-and-play framework, we plug two Gaussian-mixture-based denoisers into the iterations of an alternating direction method of multipliers (ADMM): a spatial regularizer learned from the observed multispectral image, and a spectral regularizer trained using the hyperspectral data. The proposed approach achieves very competitive results, improving the performance over using a single regularizer. Furthermore, the spectral regularizer can be used to classify the image pixels, opening the door to class-adapted models.

Keywords

Data fusion Hyperspectral imaging Spatial-spectral regularization Plug-and-play Gaussian mixture 

Notes

Acknowledgments

This work was partially supported by Fundação para a Ciência e Tecnologia (FCT), grants BD/102715/2014, UID/EEA/5008/2013, and ERANETMED/0001/2014. The authors would like to thank Prof. N. Yokoya for providing the datasets [26].

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Afonso M. Teodoro
    • 1
  • José M. Bioucas-Dias
    • 1
  • Mário A. T. Figueiredo
    • 1
    Email author
  1. 1.Instituto de Telecomunicações, Instituto Superior TécnicoUniversidade de LisboaLisbonPortugal

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