Gabor Functions for Interpolation on Hexagonal Lattice

An interpolation model using Gabor Filter is demonstrated on hexagonally sampled data, which outperform classical B-splines and MOMS. Our method has optimal approximation theoretic performances, for a good quality image. The computational cost is considerably low when compared to similar processing in the rectangular domain. In this paper the parameter sigma of 2/pi is found to satisfy most of the image interpolation requirements in terms of high Peak Signal-to-Noise Ratio (PSNR) , lower Mean Squared Error (MSE) and better image quality by adopting a windowing technique. I. Introduction Image reconstruction through interpolation is routine task in image processing during all transformation that is made on an image. Such transformations include scaling, rotation, registration, and edge detection. Considerable interest on hexagonal sampling is shown recently due to the interest in human vision systems, some of the image acquisition systems using hexagonally laid pixels, imaging radars and nuclear medicine. They also possess better topological and geometrical properties, resulting in a more efficient signal representation in two dimensions [1,2-4]. Interpolation is the process of estimation of the underlying representative function of the data points on a lattice. Here we consider the nodes corresponding to