A neural implementation of interpolation with a family of kernels
A paradigm for interpolating images based on a family of kernels is presented. Each kernel is "tuned" to specific image characteristics and contains the information responsible for the local creation of missing detail. This interpolation process (1) exploits the correlation that exists in the local structure of images via a self-organizing feature map (SOFM) and (2) establishes an optimal set of linear associative memories (LAMs) from the homologous neighborhoods of a set of low and high resolution image counterparts. Each LAM creates members of the family of interpolation kernels. We compare the performance of this technique with the commonly used bilinear and spline interpolation methods and demonstrate its ability to generalize well.