title: Sparse bayesian learning of filters for efficient image expansion.
abstract: We propose a framework for expanding a given image using an interpolator that is trained in advance with training data, based on sparse bayesian estimation for determining the optimal and compact support for efficient image expansion. Experiments on test data show that learned interpolators are compact yet superior to classical ones.
authors.affiliation: Graduate School of Informatics, Kyoto University, Kyoto 611-0011, Japan. atsu-kan@sys.i.kyoto-u.ac.jp
authors.forename: Atsunori
authors.initials: A
authors.lastname: Kanemura
authors.forename: Shin-ichi
authors.initials: S
authors.lastname: Maeda
authors.forename: Shin
authors.initials: S
authors.lastname: Ishii
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; D012815:Signal Processing, Computer-Assisted