Variational Bayesian Pansharpening with Super-Gaussian Sparse Image Priors Pérez Bueno, Fernando Vega López, Miguel Mateos Delgado, Javier Molina Soriano, Rafael Katsaggelos, Aggelos K. Pansharpening Variational Bayesian Image fusion Super-Gaussians Pansharpening 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. 2020-12-09T12:22:56Z 2020-12-09T12:22:56Z 2020-09-16 info:eu-repo/semantics/article Pérez-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] http://hdl.handle.net/10481/64766 10.3390/s20185308 eng http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess Atribución 3.0 España Mdpi