Improving the Discretization Step of Multidimensional Digital Arrays through Self-Tuned Extrapolation

Adaptive extrapolation-based algorithms for upscaling multidimensional digital arrays are investigated. For each array element an atomic extrpolator is chosen from a set of computationally simple atomic extrapolators. The choise relies on the local variation ratio in different directions. The adaptation suggests the efficient automatic selection of the local variation ratio limit at which one atomic extrapolator is replaced by another. The computational efficiency of the self-adjustment algorithm is determined by the use of preceeding (neighboring) values of the extrapolator accuracy factor in calculating the local variation ratio limit. Higher accuracy of the extrapolator is due to the use of the downscaled version of the input multidimensional data array for adjusting the extrapolator. The higher efficiency of adaptive extrapolation when scaling multidimensional data arrays has been experimentally proven.