Approximations Of Posterior Distributions In Blind Deconvolution (2008)
Using Variational Methods, Javier Mateos, Rafael Molina
In this paper the blind deconvolution problem is formulated using the variational framework. With its use approximations of the involved probability distributions are developed resulting in two...
High-Resolution Color Image Reconstruction from (2008)
Compressed Video Sequences, Javier Mateos, Aggelos K. Katsaggelos, Rafael Molina
In this work we propose an algorithm for the estimation of high resolution color frames from a low resolution compressed color video sequence. The algorithm exploits the existing correlation between...
Image Models and Their Role in Image Processing. Some Multichannel Results. (2007)
Rafael Molina And, Rafael Molina, Javier Mateos
Over the last few years an enormous amount of research has been devoted to restore astronomical images, but not much work has been reported on the use of multichannel techniques to restore such...
ABAYESIAN APPROACH TO BLIND DECONVOLUTION BASED ON DIRICHLET DISTRIBUTIONS (2007)
Rafael Molina, Aggelos K. Katsaggelos, Javier Abad, Javier Mateos
This paper deals with the simultaneous identi#cation of the blur and the restoration of a noisy and blurred im# age. We propose the use of Dirichlet distributions to model our prior knowledge about...
High-Resolution Color Image Reconstruction from Compressed Video Sequences (2007)
Javier Mateos, Aggelos K. Katsaggelos, Rafael Molina
In this work we propose an algorithm for the estimation of high resolution color frames from a low resolution compressed color video sequence. The algorithm exploits the existing correlation between...
Super resolution of multispectral images using locally adaptive models (2007)
Rafael Molina, Javier Mateos, Miguel Vega, Aggelos K. Katsaggelos
In this paper we present a locally adaptive super resolution Bayesian methodology for pansharpening of multispectral images. The proposed method incorporates prior local knowledge on the expected...
Blind deconvolution using a variational approach to parameter, image, and blur estimation (2006)
Rafael Molina, Javier Mateos, Aggelos K. Katsaggelos
Abstract—Following the hierarchical Bayesian framework for blind deconvolution problems, in this paper, we propose the use of simultaneous autoregressions as prior distributions for both the image...
Bayesian resolution enhancement of compressed video (2004)
C. Andrew Segall, Aggelos K. Katsaggelos, Rafael Molina, Javier Mateos
Abstract—Super-resolution algorithms recover high-frequency information from a sequence of low-resolution observations. In this paper, we consider the impact of video compression on the...
An overview of low noise devices and associated circuits for 100-200 GHz space applications (2003)
Dambrine, G., Parenty, Thierry, Bollaert, S., Happy, Henri, Cappy, A., Mateos, Javier, ...
This paper relates the state of the art of the HEMT low noise technologies for the space applications at millimeter wave and specially for the earth observation in the G-band (140 – 220 GHz)).The...
Bayesian Multichannel Image Restoration UsingCompound Gauss-Markov Random Fields (2003)
Rafael Molina, Javier Mateos, Aggelos K. Katsaggelos, Miguel Vega
In this paper, we develop a multichannel image restoration algorithm using Compound Gauss Markov Random Fields (CGMRF) models. The line process in the CGMRF will allow the channels to share important...
Bayesian Parameter Estimation in Image Reconstruction from Subsampled Blurred Observations (2003)
Miguel Vega, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos
In this paper we consider the estimation of the unknown hyperparameters for the problem of reconstructing a high-resolution image from multiple undersampled, shifted, blurred and degraded frames with...
SPECT image reconstruction using compound models (2002)
Antonio L Ópez, Rafael Molina, Javier Mateos, Aggelos K. Katsaggelos
We propose a new iterative method for Maximum a Posteriori (MAP) reconstruction of SPECT (Single Photon Emission Computed Tomography) images. The method uses Compound Gauss Markov Random Fields...
Hyperparameter estimation in image restoration problems with partially-known blurs (2002)
Nikolas P. Galatsanos, Vladimir Z. Mesarović, Rafael Molina, Member Spie, Aggelos K. Katsaggelos, Javier Mateos
with partially known blurs
C. Andrew Segall, Rafael Molina, Aggelos K. Katsaggelos, Javier Mateos
A framework for recovering high-resolution information from a sequence of sub-sampled and compressed observations is presented. Compression schemes that describe a video sequence through a...
A General Multichannel Image Restoration Method Using Compound Models (2002)
Rafael Molina Javier, Javier Mateos, Aggelos K. Katsaggelos, Miguel Vega
In this paper we present a multichannel image restoration method using Compound Gauss Markov Random Field (CGMRF) models. Information regarding the objects present in the scene is shared via the line...
Bayesian high-resolution reconstruction of low-resolution compressed video (2001)
C. Andrew Segall, Rafael Molina, Aggelos K. Katsaggelos, Javier Mateos
A method for simultaneously estimating the high-resolution frames and the corresponding motion field from a compressed low-resolution video sequence is presented. The algorithm incorporates knowledge...
Bayesian High-Resolution Reconstruction Of (2001)
C. Andrew Segall, Rafael Molina, Aggelos K. Katsaggelos, Javier Mateos
A method for simultaneously estimating the high-resolution frames and the corresponding motion field from a compressed low-resolution video sequence is presented. The algorithm incorporates knowledge...
Image restoration in astronomy: a Bayesian perspective (2001)
Molina, Rafael, Cortijo, Francico José, Mateos, Javier
When preparing an article on image restoration in astronomy, it is obvious that some topics have to be dropped to keep the work at reasonable length. We have decided to concentrate on image and noise...
Design optimisation of ultra-short gate HEMTS using MONTE CARLO simulation (2000)
Mateos, Javier, González, Tomas, Pardo, Daniel
By using a Monte Carlo simulator the static and dynamic characteristics of a 50 nm gate AlInAs/GaInAs δ−doped HEMTs are investigated. The Monte Carlo model includes some important effects that are...
Color image restoration using compound Gauss-Markov random fields (2000)
Javier Mateos, Aggelos K. Katsaggelos, Rafael Molina
In this work we extend the use of Compound Gauss Markov Random Fields to the restoration of color images. While most of the work in color image restoration is concentrated on enforcing similarity...
Color Image Restoration Using Compound Gauss-Markov (2000)
Random Fields Javier, Javier Mateos, Aggelos K. Katsaggelos, Rafael Molina
In this work we extend the use of Compound Gauss Markov Random Fields to the restoration of color images. While most of the work in color image restoration is concentrated on enforcing similarity...
Javier Mateos, Aggelos K. Katsaggelos, Rafael Molina
Abstract—With block-based compression approaches for both still images and sequences of images annoying blocking artifacts are exhibited, primarily at high compression ratios. They are due to the...
Bayesian and regularization methods for hyperparameter estimation in image restoration (1999)
Rafael Molina, Aggelos K. Katsaggelos, Javier Mateos
Abstract — In this paper, we propose the application of the hierarchical Bayesian paradigm to the image restoration problem. We derive expressions for the iterative evaluation of the two...
Estimating and Transmitting Regularization Parameters for Reducing Blocking Artifacts (1997)
Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos
High compression ratios for both still images and sequences of images are usually achieved by quantizing the block discrete cosine transform #BDCT # coe#cients of the intensity or displaced frame...
A Bayesian Approach To Blind Deconvolution Based On Dirichlet Distributions (1997)
Rafael Molina, Aggelos K. Katsaggelos, Javier Abad, Javier Mateos
This paper deals with the simultaneous identification of the blur and the restoration of a noisy and blurred image. We propose the use of Dirichlet distributions to model our prior knowledge about...
Bayesian Image Estimation from an Incomplete Set of Blurred, Undersampled Low Resolution Images
Javier Mateos, Miguel Vega, Rafael Molina, Aggelos K. Katsaggelos
This paper deals with the problem of reconstructing a highresolution image from an incomplete set of undersampled, blurred and noisy images shifted with subpixel displacement. We derive mathematical...