title: Super-resolution image reconstruction using non-parametric Bayesian INLA approximation.
abstract: Superresolution are techniques to enhance the resolution of an image without changing the camera resolution, through using software algorithms. In this context, this paper proposes a fully automatic Superresolution algorithm, using a recent non-parametric Bayesian inference method based on numerical integration, known in the statistical literature as Integrated Nested Laplace Approximation. By applying such inference method to the Superresolution problem, this paper shows that all the equations needed to implement this technique can be written in closed form. Moreover, the results of several simulations (three of them are here presented) show that the proposed algorithm performs better than other Superresolution algorithms recently proposed. As far as the authors know, this is the first time that the Integrated Nested Laplace Approximation is used in the area of image processing, which is a meaningful contribution of this paper.
authors.forename: Marcelo Oliveira
authors.initials: MO
authors.lastname: Camponez
authors.forename: Ottoni Teatini Salles
authors.initials: OT
authors.lastname: Evandro
authors.forename: Mário
authors.initials: M
authors.lastname: Sarcinelli-Filho
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity