A Super-Resolution Image Reconstruction using Natural Neighbor Interpolation

A super-resolution image reconstruction algorithm using natural neighbor interpolation is proposed and its performance is evaluated. The algorithm is divided into two stages: image registration and the reconstruction of a high-resolution color image. In the first stage, as shifts between images are usually unknown, the algorithm computes an approximation of these displacements by solving the system of linear equations proposed by Keren, Peleg, and Brada, then the pixels of all low-resolution images are mapped into a highresolution grid by computing the new coordinates using the motion vectors. In the second stage, the pixel values that match the high-resolution grid are interpolated using natural neighbor interpolation which is a weighted average interpolation method for scattered data, based in the areas of the Voronoi polygons of the neighboring pixels. Finally, the proposed natural neighbor superresolution algorithm is compared with some popular super-resolution algorithms presented in literature.