Image interpolation using classification-based neural networks

Abstract

Standard image interpolation methods generally use a uniform interpolation filter on the entire image. To achieve a better performance on specific structures, some content adaptive interpolation methods, such as Kondo's method (1), have been introduced. However, these content adaptive methods are limited to fit image data into a linear model in each class. We investigate replacing the linear model by a flexible non-linear model, such as a feed-forward neural network. This results in a new interpolation algorithm based on known classification, but achieving better results. In this paper, such a classification-based neural network approach and its evaluation are presented. Both objective and subjective image quality results indicate that the proposed method gives an additional improvement in the interpolated image quality 1 .