Abstract
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.
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Notes
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References
Bauschke, H., Combettes, P.: Convex Analysis and Monotone Operator Theory in Hilbert Spaces. Springer, New York (2011)
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)
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)
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)
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)
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)
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)
Teodoro, A., Almeida, M., Figueiredo, M.: Single-frame image denoising and inpainting using Gaussian mixtures. In: ICPRAM, pp. 283–288 (2015)
Teodoro, A., Bioucas-Dias, J., Figueiredo, M.: Image restoration and reconstruction using variable splitting and class-adapted image priors. In: IEEE-ICIP (2016)
Teodoro, A., Bioucas-Dias, J., Figueiredo, M.: Image restoration with locally selected class-adapted models. In: IEEE-MLSP (2016)
Venkatakrishnan, S., Bouman, C., Chu, E., Wohlberg, B.: Plug-and-play priors for model based reconstruction. In: IEEE GlobalSIP, pp. 945–948 (2013)
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)
Zoran, D., Weiss, Y.: From learning models of natural image patches to whole image restoration. In: IEEE-CVPR, pp. 479–486 (2011)
Acknowledgments
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.
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Teodoro, A., Bioucas-Dias, J., Figueiredo, M. (2017). Sharpening Hyperspectral Images Using Plug-and-Play Priors. In: Tichavský, P., Babaie-Zadeh, M., Michel, O., Thirion-Moreau, N. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2017. Lecture Notes in Computer Science(), vol 10169. Springer, Cham. https://doi.org/10.1007/978-3-319-53547-0_37
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DOI: https://doi.org/10.1007/978-3-319-53547-0_37
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