Bayesian image superresolution and hidden variable modeling

Abstract

Superresolution is an image processing technique that estimates an original high-resolution image from its low-resolution and degraded observations. In superresolution tasks, there have been problems regarding the computational cost for the estimation of high-dimensional variables. These problems are now being overcome by the recent development of fast computers and the development of powerful computational techniques such as variational Bayesian approximation. This paper reviews a Bayesian treatment of the superresolution problem and presents its extensions based on hierarchical modeling by employing hidden variables.