Improved cubic convolution for two dimensional image reconstruction

This paper describes improved piecewise cubic convolution for two-dimensional image reconstruction. Piecewise cubic convolution is one of the most popular methods for image reconstruction, but the traditional approach uses a separable two-dimensional convolution kernel that is based on a one-dimensional derivation. The traditional approach is suboptimal for the usual case of non-separable scenes and systems. The improved approach implements the most general two-dimensional, non-separable, piecewise cubic interpolator with constraints for symmetry, continuity, and smoothness.