LFGPC: Learning Optimal Filters for Gaussian Process Classification
A Matlab program that implements the method for learning optimal filters for Gaussian process classification in P. Ruiz, R. Molina, and A.K. Katsaggelos, “Joint Data Filtering and Labeling using Gaussian Processes and Alternating Direction Method of Multipliers”, IEEE Transactions on Image Processing, 2016. doi: 10.1109/TIP.2016.2558472.
Fast Blind Deconvolution with Huber Super Gaussian Priors
A Matlab program that implements the fast blind deconvolution method in X. Zhou, M. Vega, F. Zhou, R. Molina and A. Katsaggelos, “Fast Blind Deconvolution with Huber Super Gaussian Priors”, in Digital Signal Processing 2016, doi: 10.1016/j.dsp.2016.08.008.
Variational Dirichlet Blur Kernel Estimation
A Matlab program that implements the variational Dirichlet blur kernel estimation method in X. Zhou, J. Mateos, F. Zhou, R. Molina, and A.K. Katsaggelos, “Variational Dirichlet Blur Kernel Estimation”, IEEE Transactions on Image Processing, 2015. doi: 10.1109/TIP.2015.2478407
Bayesian Active Learning for Remote Sensing
A Matlab program that implements the Bayesian Active Learning (BAL) method applied to Remote Sensing images published in P. Ruiz, J. Mateos, G. Camps-Valls, R. Molina, and A.K. Katsaggelos, “Bayesian Active Remote Sensing Image Classification”, IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 4, 2186-2196, April 2014.
Sparse Bayesian Methods for Low-Rank Matrix Estimation
A Matlab program that implements the low-rank matrix estimation method in D. Babacan, M. Luessi, R. Molina, and A.K. Katsaggelos, , “Sparse Bayesian Methods for Low-Rank Matrix Estimation”, IEEE Transaction on Signal Processing, vol. 60, no. 8, 3964-3977, 2012.
Super resolution softwareA Matlab program with graphical user interface that implements several image super-resolution methods developed in the project "Super-resolución bayesiana de imágenes aplicada a vigilancia y seguridad". It implements several SR methods including the presented in the following papers:
The software and manual can be downloaded from the Super-resolution project page.
- S. Villena, M. Vega, D. Babacan, R. Molina, and A. Katsaggelos. “Bayesian combination of sparse and non sparse priors in image superresolution”, Digital Signal Processing, vol. 23, no. 2, 530-541, 2013.
- S. Villena, M. Vega, R. Molina, and A. K. Katsaggelos, "Bayesian super-resolution image reconstruction using an l1 prior," in 6th International Symposium on Image and Signal Processing and Analysis (ISPA 2009) Best paper award, Image Processing and Analysis Track, 2009, pp. 152-157.
- S. Villena, M. Vega, D. Babacan, R. Molina, and A. Katsaggelos. Using the Kullback-Leibler divergence to combine image priors in super-resolution image reconstruction. In IEEE International Conference on Image Processing, pages 809-812. Hong-Kong (China), September 2010.
- S. D. Babacan, R. Molina, and A.K. Katsaggelos. Variational Bayesian super resolution. IEEE Transactions on Image Processing, 20(4):984-999, 2011.
Blind image restoration
Developed by Cora Beatriz Pérez Ariza and José Manuel Llamas Sánchez under the direction of Prof. Rafael Molina and Prof. Javier Mateos, implements the algorithms in the paper R. Molina, J. Mateos and A. K. Katsaggelos “Blind Deconvolution using a variational approach to parameter, image, and blur estimation,” IEEE Trans. on Image Processing, vol. 15, no. 12, 3715-3727, December 2006.