We show how improved sequences for magnetic resonance imaging can be found through optimization of Bayesian design scores. Combining approximate Bayesian inference and natural ima...
Matthias W. Seeger, Hannes Nickisch, Rolf Pohmann,...
In this paper we present a new super resolution Bayesian method for pansharpening of multispectral images which: a) incorporates prior knowledge on the expected characteristics of...
Rafael Molina, Miguel Vega, Javier Mateos, Aggelos...
In this paper, we study unsupervised image segmentation using wavelet-domain hidden Markov models (HMMs). We first review recent supervised Bayesian image segmentation algorithms ...
Abstract. Imaging is a class of non-Bayesian methods for the revision of probability density functions originally proposed as a semantics for conditional logic. Two of these revisi...
Abstract. This paper deals with the problem of reconstructing a highresolution image from an incomplete set of undersampled, blurred and noisy images shifted with subpixel displace...
Javier Mateos, Miguel Vega, Rafael Molina, Aggelos...