Multidimensional Signal Interpolation Based on Factorization and Dimension Reduction of Decision Rules

Abstract

We research adaptive multidimensional signal interpolators based on switching between several interpolating functions at each signal sample. We perform the switching by decision rule, which is optimized for each signal in the parameter space of this decision rule. Algorithms for factorization and dimension reduction of decision rules are proposed. We investigate new classes of interpolating functions and systems of local features. We propose fitting procedures for adaptive interpolators. We perform the software implementation of the developed algorithms. A numerical experiment in natural multidimensional signals (video, remote sensing data and hyperspectral data) confirms the gain of the adaptive interpolator.