In this paper we propose a novel algorithm for super resolution based on total variation prior and variational distribution approximations. We formulate the problem using a hierar...
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag...
With the term super-resolution we refer to the problem of reconstructing an image of higher resolution than that of unregistered and degraded observations. Typically, the reconstru...
In this paper, we present a patch-based variational Bayesian framework of image processing using the language of factor graphs (FGs). The variable and factor nodes of FGs represen...
In this paper we propose two fast Total Variation (TV) based algorithms for image restoration by utilizing variational posterior distribution approximation. The unknown image and ...
Bruno Amizic, S. Derin Babacan, K. Michael Ng, Raf...
This paper studies a variational Bayesian unmixing algorithm for hyperspectral images based on the standard linear mixing model. Each pixel of the image is modeled as a linear com...
Olivier Eches, Nicolas Dobigeon, Jean-Yves Tourner...