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, â€œSuper 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, â€œSuper 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.

zalteck/SG_Pansharpening

Variational Bayesian Pansharpening with Super-Gaussian Sparse Image Priors.