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 dis-placements 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 high-resolution 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 super-resolution algorithm is compared with some popular super-resolution algorithms presented in literature.