Variational Bayesian Pansharpening with Super-Gaussian Sparse Image Priors.

This directory holds implementations for the method proposed in the paper

        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. 
        https://www.mdpi.com/1424-8220/20/18/5308
        
        and also:
        M. Vega, J. Mateos, R. Molina, and A. K. Katsaggelos, “Super resolution of multispectral images using TV image models, 
        in International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2008, pp. 408-415.

Directories:

others
    This directory holds other's authors implementations for some methods.
    These implementations have been taken from https://rscl-grss.org/coderecord.php?id=541, used in

   Vivone, G.; Alparone, L.; Chanussot, J.; Dalla Mura, M.; Garzelli, A.; Licciardi, G.A.; Restaino, R.; Wald, L. 
    A critical comparison among pansharpening algorithms. IEEE Trans. Geosci. Remote Sens. 2015, 53, 2565-2586.

source
    This directory holds implementations for the methods proposed in papers

        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. 

        M. Vega, J. Mateos, R. Molina, and A. K. Katsaggelos, “Super resolution of multispectral images using TV image models, 
        in International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, 2008, pp. 408-415.

work
    This directory holds functions and scripts to perform all experiments in Pérez-Bueno, F. 2020 paper.

    Directories whithin work:

        Sensors
            Datasets   (MS image Dataset for Pérez-Bueno, F. 2020 paper). Please download from:  https://drive.google.com/drive/folders/1oX25xQZ7eoVW-rvGWR7T36VGLXKwwb-K?usp=sharing
            data       (Input data files for all experiments. dododoMain_Reduced_Resolution obtains those files from Dataset)
            results    (Folder to store output data files for all experiments)
            
        Quality_Indices  (Functions to obtain quality indexes coming from Vivone 2015 paper (See https://rscl-grss.org/coderecord.php?id=541))

Functions:

NOTE: Datasets folder must be filled with the .mat files downloaded and data folder content has to be obtained before using the functions below:

Recontructions of a Multi-spectral image using other authors methods and comparison with groundtruth following the Wald's protocol can be obtained using doothersSens0 function.

Recontructions of a Multi-spectral image using other authors methods and quantitative comparison using QNR measures can be obtained using doothersSens0FR function.

Recontructions of a Multi-spectral image using TV, log and lp methods and comparison with groundtruth following the Wald's protocol can be obtained using doTVMESens0, dologSGMESens0 and dolpSGMESens0 respectively.

Recontructions of a Multi-spectral image using TV, log and lp methods can be obtained using doTVMESens0FR, dologSGMESens0FR and dolpSGMESens0FR respectively.