Image interpolation using classification-based neural networks
Hao Hu, P.M. Holman, Geert de Haan · 2005
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. Index Terms — image interpolation, neural network, non-linear models, classification