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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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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