R
Rafael Molina
Researcher at University of Granada
Publications - 398
Citations - 11970
Rafael Molina is an academic researcher from University of Granada. The author has contributed to research in topics: Image restoration & Iterative reconstruction. The author has an hindex of 52, co-authored 381 publications receiving 10765 citations. Previous affiliations of Rafael Molina include Intel & Northwestern University.
Papers
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Proceedings ArticleDOI
Sparse Bayesian image restoration
TL;DR: A novel Bayesian algorithm is proposed where Gaussian distributions are placed per pixel in the high-pass filter outputs of the image to estimate the unknown image and hyperparameters for both the image prior and the image degradation noise.
Journal ArticleDOI
Bayesian deconvolution with prior knowledge of object location : applications to ground-based planetary images
TL;DR: In this article, a Bayesian method to deconvolve images when the location of the objects in the image is known in advance is presented, and this knowledge of location is incorporated into the prior model via a labeling process.
Proceedings ArticleDOI
Generalized Gaussian Markov random field image restoration using variational distribution approximation
TL;DR: Novel algorithms for image restoration and parameter estimation with a generalized Gaussian Markov random field prior utilizing variational distribution approximation and two algorithms resulting from this formulation which provide approximations to the posterior distributions of the latent variables are developed.
Book ChapterDOI
Super Resolution of Multispectral Images Using TV Image Models
TL;DR: A novel algorithm for the pansharpening of multispectral images based on the use of a Total Variation (TV) image prior is proposed and uses the sensor characteristics to model the observation process of both panchromatic and multisectral images.
Proceedings ArticleDOI
Blind restoration of blurred photographs via AR modelling and MCMC
TL;DR: A new image and blur prior model is proposed, based on non-stationary autoregressive (AR) models, and these are used to blindly deconvolve blurred photographic images, using the Gibbs sampler.