@misc{10481/64766,
year = {2020},
month = {9},
url = {http://hdl.handle.net/10481/64766},
abstract = {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.},
organization = {Spanish Ministerio de Economia y Competitividad 	
DPI2016-77869-C2-2-R},
organization = {Instituto de Salud Carlos III

Spanish Government

PID2019-105142RB-C22},
organization = {Visiting Scholar Program at the University of Granada},
publisher = {Mdpi},
keywords = {Pansharpening},
keywords = {Variational Bayesian},
keywords = {Image fusion},
keywords = {Super-Gaussians},
title = {Variational Bayesian Pansharpening with Super-Gaussian Sparse Image Priors},
doi = {10.3390/s20185308},
author = {Pérez Bueno, Fernando and Vega López, Miguel and Mateos Delgado, Javier and Molina Soriano, Rafael and Katsaggelos, Aggelos K.},
}