An Algebraic Nonuniformity Correction Algorithm for Hexagonally-Sampled Infrared Images: A Simulation Study

Infrared imagery, like almost any other two-dimensional (2-D) imagery, have been traditionally sampled and acquired using a rectangular grid. Algebraic fixed-pattern noise/ nonuniformity correction (NUC) algorithms work on this traditional rectangular grid mitigating the most dominant, bias/offset portion of the nonuniformity. On the other hand, it is well-known that hexagonal sampling grid captures more information in sampled data/imagery when compared to traditional rectangular sampling, and a hexagonal addressing scheme for hexagonally-sampled imagery, namely array set addressing scheme, was recently developed. In this work, we derive the bilinear interpolation equations between two image frames for hexagonally-sampled infrared imagery with bias/offset nonuniformity under the 2-D global motion of the scene or the camera, and apply the 2-D algebraic NUC algorithm to hexagonally-sampled imagery. We present a simulation of mid-wave infrared imagery with hexagonally-sampled pixel array with bias nonuniformity under simulated global translational motion, and we test the efficiency of the NUC algorithm on the simulated infrared imagery and compare the performance of the hexagonally-sampled pixel array imagery NUC results to those of the traditional rectangularly-sampled pixel array imagery.