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dc.contributor.authorPérez Bueno, Fernando 
dc.contributor.authorVega López, Miguel 
dc.contributor.authorMateos Delgado, Javier 
dc.contributor.authorMolina Soriano, Rafael 
dc.contributor.authorKatsaggelos, Aggelos K.
dc.identifier.citationPérez-Bueno, F., Vega, M., Mateos, J., Molina, R., & Katsaggelos, A. K. (2020). Variational Bayesian Pansharpening with Super-Gaussian Sparse Image Priors. Sensors, 20(18), 5308. [doi:10.3390/s20185308]es_ES
dc.description.abstractPansharpening is a technique that fuses a low spatial resolution multispectral image and a high spatial resolution panchromatic one to obtain a multispectral image with the spatial resolution of the latter while preserving the spectral information of the multispectral image. In this paper we propose a variational Bayesian methodology for pansharpening. The proposed methodology uses the sensor characteristics to model the observation process and Super-Gaussian sparse image priors on the expected characteristics of the pansharpened image. The pansharpened image, as well as all model and variational parameters, are estimated within the proposed methodology. Using real and synthetic data, the quality of the pansharpened images is assessed both visually and quantitatively and compared with other pansharpening methods. Theoretical and experimental results demonstrate the effectiveness, efficiency, and flexibility of the proposed formulation.es_ES
dc.description.sponsorshipSpanish Ministerio de Economia y Competitividad DPI2016-77869-C2-2-Res_ES
dc.description.sponsorshipInstituto de Salud Carlos III Spanish Government PID2019-105142RB-C22es_ES
dc.description.sponsorshipVisiting Scholar Program at the University of Granadaes_ES
dc.rightsAtribución 3.0 España*
dc.subjectVariational Bayesianes_ES
dc.subjectImage fusiones_ES
dc.titleVariational Bayesian Pansharpening with Super-Gaussian Sparse Image Priorses_ES

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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España