SUPER RESOLUTION USING RADIAL BASIS NEURAL NETWORKS


Catalbas M. C. , ÖZTÜRK S.

21st Signal Processing and Communications Applications Conference (SIU), CYPRUS, 24 - 26 April 2013 identifier identifier

Abstract

The output of image size enlargement has important differences compared to the original sized image. In this study, an algorithm which intends to minimize the loss due to these differences, is presented. This minimization process is provided by radial bases neural networks (RBNN). In order to achieve better performance the RBNN activation function radius criteria is chosen adaptively throughout the work. It is observed that this new proposed method achieves better performance than that of methods in the literature. With the use of this method, it is foreseen that human made mistakes in disease diagnosis like computer tomography, inwhich small details are important, will be reduced.