Sharpening Hyperspectral Images Using Plug-and-Play Priors

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10169)


This paper addresses the problem of fusing hyperspectral (HS) images of low spatial resolution and multispectral (MS) images of high spatial resolution into images of high spatial and spectral resolution. By assuming that the target image lives in a low dimensional subspace, the problem is formulated with respect to the latent representation coefficients. Our major contributions are: (i) using patch-based spatial priors, learned from the MS image, for the latent images of coefficients; (ii) exploiting the so-called plug-and-play approach, wherein a state-of-the-art denoiser is plugged into the iterations of a variable splitting algorithm.


Data fusion Hyperspectral imaging Multispectral imaging Latent variables Plug-and-play Gaussian mixtures 



This work was partially supported by Fundação para a Ciência e Tecnologia (FCT), grants UID/EEA/5008/2013, ERANETMED/0001/2014 and BD/102715/2014.


  1. 1.
    Bauschke, H., Combettes, P.: Convex Analysis and Monotone Operator Theory in Hilbert Spaces. Springer, New York (2011)CrossRefzbMATHGoogle Scholar
  2. 2.
    Bioucas-Dias, J., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., Chanussot, J.: Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens. 5, 354–379 (2012)CrossRefGoogle Scholar
  3. 3.
    Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3, 1–122 (2011)CrossRefzbMATHGoogle Scholar
  4. 4.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3D transform-domain collaborative filtering. IEEE Trans. Image Proc. 16, 2080–2095 (2007)CrossRefGoogle Scholar
  5. 5.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Color image denoising via sparse 3D collaborative filtering with grouping constraint in luminance-chrominance space. In: IEEE ICIP (2007)Google Scholar
  6. 6.
    Loncan, L., Almeida, L., Bioucas-Dias, J., Briottet, X., Chanussot, J., Dobigeon, N., Fabre, S., Liao, W., Licciardi, G., Simões, M., Tourneret, J.-Y., Veganzones, M., Vivone, G., Wei, Q., Yokoya, N.: Hyperspectral pansharpening: a review. IEEE Geosci. Remote Sens. Mag. 3, 27–46 (2015)CrossRefGoogle Scholar
  7. 7.
    Simões, M., Bioucas-Dias, J., Almeida, L., Chanussot, J.: A convex formulation for hyperspectral image superresolution via subspace-based regularization. IEEE Trans. Geosci. Remote Sens. 55, 3373–3388 (2015)CrossRefGoogle Scholar
  8. 8.
    Teodoro, A., Almeida, M., Figueiredo, M.: Single-frame image denoising and inpainting using Gaussian mixtures. In: ICPRAM, pp. 283–288 (2015)Google Scholar
  9. 9.
    Teodoro, A., Bioucas-Dias, J., Figueiredo, M.: Image restoration and reconstruction using variable splitting and class-adapted image priors. In: IEEE-ICIP (2016)Google Scholar
  10. 10.
    Teodoro, A., Bioucas-Dias, J., Figueiredo, M.: Image restoration with locally selected class-adapted models. In: IEEE-MLSP (2016)Google Scholar
  11. 11.
    Venkatakrishnan, S., Bouman, C., Chu, E., Wohlberg, B.: Plug-and-play priors for model based reconstruction. In: IEEE GlobalSIP, pp. 945–948 (2013)Google Scholar
  12. 12.
    Wei, Q., Bioucas-Dias, J., Dobigeon, N., Tourneret, J.-Y.: Hyperspectral and multispectral image fusion based on a sparse representation. IEEE Trans. Geosci. Remote Sens. 53, 3658–3668 (2015)CrossRefGoogle Scholar
  13. 13.
    Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: IEEE-CVPR, pp. 479–486 (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Instituto de TelecomunicaçõesLisbonPortugal
  2. 2.Instituto Superior Técnico, Universidade de LisboaLisbonPortugal

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